Joshua's Blog
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I am going to maintain this page to record a few things about computer vision that I have read, am doing, or will have a look at. Previously I’d like to write short notes of the papers that I have read. It is a good way to remember and understand the ideas of the authors. But gradually I found that I forget much portion of what I had learnt because in addition to paper I also derive knowledges from others’ blogs, online courses and reports, not recording them at all. Besides, I need a place to keep a list of what I should have a look at but do not at the time when I discover them. This page will be much like a catalog.

PAPERS AND PROJECTS

OBJECT/SALIENCY DETECTION

  • EfficientDet: Scalable and Efficient Object Detection (PDF, Project/Code)
  • YOLOv4: Optimal Speed and Accuracy of Object Detection (PDF, Project/Code)
  • Learning Data Augmentation Strategies for Object Detection (PDF, Project/Code)
  • Light-Weight RetinaNet for Object Detection (PDF)
  • Objects as Points (PDF, Code/Projects)
  • Augmentation for small object detection (PDF)
  • ThunderNet: Towards Real-time Generic Object Detection (PDF)
  • Pyramid Mask Text Detector (PDF)
  • Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving (PDF)
  • CornerNet: Detecting Objects as Paired Keypoints (PDF, Code/Project, Reading Note)
  • Scale-Aware Trident Networks for Object Detection (PDF)
  • Acquisition of Localization Confidence for Accurate Object Detectinon (PDF, Project/Code)
  • A Single Shot Text Detector with Scale-adaptive Anchors (PDF)
  • Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation (PDF)
  • Object detection at 200 Frames Per Second (PDF, )
  • DetNet: A Backbone network for Object Detection (PDF, Reading Note)
  • Zero-Shot Object Detection (PDF)
  • Unsupervised Discovery of Object Landmarks as Structural Representations (PDF, Project/Code)
  • Cascade R-CNN: Delving into High Quality Object Detection (PDF, PROJECT/CODE)
  • Path Aggregation Network for Instance Segmentation (PDF)
  • ClickBAIT-v2: Training an Object Detector in Real-Time (PDF)
  • Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection (PDF)
  • Complex-YOLO: Real-time 3D Object Detection on Point Clouds (PDF)
  • Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts (PDF)
  • Domain Adaptive Faster R-CNN for Object Detection in the Wild (PDF)
  • Chinese Text in the Wild (PDF, Project/Code)
  • TSSD: Temporal Single-Shot Detector Based on Attention and LSTM for Robotic Intelligent Perception (PDF)
  • Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection (PDF, Reading Note)
  • Object Detection in Videos by Short and Long Range Object Linking (PDF)
  • Learning a Rotation Invariant Detector with Rotatable Bounding Box (PDF, Project/Code)
  • Detecting Curve Text in the Wild: New Dataset and New Solution (PDF, Project/Code)
  • Single Shot Text Detector with Regional Attention (PDF, Project/Code)
  • Single-Shot Refinement Neural Network for Object Detection (PDF, Project/Code, Reading Note)
  • $S^3$FD: Single Shot Scale-invariant Face Detector (PDF, Code/Project, Reading Note)
  • MegDet: A Large Mini-Batch Object Detector (PDF)
  • Light-Head R-CNN: In Defense of Two-Stage Object Detector (PDF)
  • Interpretable R-CNN (PDF)
  • Cascade Region Proposal and Global Context for Deep Object Detection (PDF)
  • PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection (PDF, Project/Code, Reading Note)
  • Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks (PDF, Reading Note)
  • Object Detection from Video Tubelets with Convolutional Neural Networks (PDF, Reading Note)
  • R-FCN: Object Detection via Region-based Fully Convolutional Networks (PDF, Project/Code, Reading Note)
  • SSD: Single Shot MultiBox Detector (PDF, Project/Code, Reading Note)
  • Pushing the Limits of Deep CNNs for Pedestrian Detection (PDF, Reading Note)
  • Object Detection by Labeling Superpixels(PDF, Reading Note)
  • Crafting GBD-Net for Object Detection (PDF, Projct/Code)
    code for CUImage and CUVideo, the object detection champion of ImageNet 2016.
  • Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection (PDF, Reading Note)
  • Training Region-based Object Detectors with Online Hard Example Mining (PDF, Reading Note)
  • Detecting People in Artwork with CNNs (PDF, Project/Code)
  • Deeply supervised salient object detection with short connections (PDF)
  • Learning to detect and localize many objects from few examples (PDF)
  • Multi-Scale Saliency Detection using Dictionary Learning (PDF)
  • Straight to Shapes: Real-time Detection of Encoded Shapes (PDF)
  • Weakly Supervised Cascaded Convolutional Networks (PDF, Reading Note)
  • Speed/accuracy trade-offs for modern convolutional object detectors (PDF, Reading Note)
  • Object Detection via End-to-End Integration of Aspect Ratio and Context Aware Part-based Models and Fully Convolutional Networks (PDF)
  • Feature Pyramid Networks for Object Detection (PDF, Reading Note)
  • COCO-Stuff: Thing and Stuff Classes in Context (PDF)
  • Finding Tiny Faces (PDF)
  • Beyond Skip Connections: Top-Down Modulation for Object Detection (PDF, Reading Note)
  • YOLO9000: Better, Faster, Stronger (PDF, Project/Code, Reading Note)
  • Quantitative Analysis of Automatic Image Cropping Algorithms: A Dataset and Comparative Study (PDF)
  • To Boost or Not to Boost? On the Limits of Boosted Trees for Object Detection (PDF)
  • Pixel Objectness (PDF, Project/Code, Reading Note)
  • DSSD: Deconvolutional Single Shot Detector (PDF, Reading Note)
  • A Fast and Compact Salient Score Regression Network Based on Fully Convolutional Network (PDF)
  • Wide-Residual-Inception Networks for Real-time Object Detection (PDF)
  • Zoom Out-and-In Network with Recursive Training for Object Proposal (PDF, Project/Code)
  • Improving Object Detection with Region Similarity Learning (PDF)
  • Tree-Structured Reinforcement Learning for Sequential Object Localization (PDF)
  • Weakly Supervised Object Localization Using Things and Stuff Transfer (PDF)
  • Unsupervised learning from video to detect foreground objects in single images (PDF)
  • A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection (PDF, Project/Code)
  • A Learning non-maximum suppression (PDF)
  • Real Time Image Saliency for Black Box Classifiers (PDF)
  • An Efficient Approach for Object Detection and Tracking of Objects in a Video with Variable Background (PDF)
  • RON: Reverse Connection with Objectness Prior Networks for Object Detection (PDF, Project/Code)
  • Deformable Part-based Fully Convolutional Network for Object Detection (PDF, Reading Note)
  • Recurrent Scale Approximation for Object Detection in CNN (PDF)
  • DSOD: Learning Deeply Supervised Object Detectors from Scratch (PDF, Project/Code, Reading Note)
  • PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN (PDF)
  • Focal Loss for Dense Object Detection (PDF)
  • Learning Uncertain Convolutional Features for Accurate Saliency Detection (PDF)
  • Optimizing Region Selection for Weakly Supervised Object Detection (PDF)
  • Kill Two Birds With One Stone: Boosting Both Object Detection Accuracy and Speed With adaptive Patch-of-Interest Composition (PDF)
  • Flow-Guided Feature Aggregation for Video Object Detection (PDF)
  • BlitzNet: A Real-Time Deep Network for Scene Understanding ([PDF]( BlitzNet: A Real-Time Deep Network for Scene Understanding), Project/Code)
  • RON: Reverse Connection with Objectness Prior Networks for Object Detection (PDF)
  • Soft Proposal Networks for Weakly Supervised Object Localization (PDF, Project/Code)
  • Feature-Fused SSD: Fast Detection for Small Objects (PDF)
  • Light Cascaded Convolutional Neural Networks for Accurate Player Detection (PDF)
  • Personalized Saliency and its Prediction (PDF)
  • WeText: Scene Text Detection under Weak Supervision (PDF)
  • VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition (PDF, Project/Code)

SEGMENTATION/PARSING

  • CenterMask: single shot instance segmentation with point representation (PDF)

  • Background Matting: The World is Your Green Screen (PDF, Project/Code, Github)

  • Towards Real-Time Automatic Portrait Matting on Mobile Devices (PDF, Project/Code)

  • Panoptic Feature Pyramid Networks (PDF)

  • Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells (PDF)

  • Deep Learning for Semantic Segmentation on Minimal Hardware (PDF)

  • TernausNetV2: Fully Convolutional Network for Instance Segmentation (PDF, Project/Code)

  • Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation (PDF, Project/Code)

  • Deep Object Co-Segmentation (PDF)

  • Fusing Hierarchical Convolutional Features for Human Body Segmentation and Clothing Fashion Classification (PDF)

  • ShuffleSeg: Real-time Semantic Segmentation Network (PDF)

  • Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation (PDF, Project/Code)

  • Learning random-walk label propagation for weakly-supervised semantic segmentation (PDF)

  • Panoptic Segmentation (PDF, Reading Note)

  • Learning to Segment Every Thing (PDF, Project/Code)

  • Deep Extreme Cut: From Extreme Points to Object Segmentation (PDF)

  • Instance-aware Semantic Segmentation via Multi-task Network Cascades (PDF, Project/Code)

  • ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation (PDF, Reading Note)

  • Learning Deconvolution Network for Semantic Segmentation (PDF, Reading Note)

  • Semantic Object Parsing with Graph LSTM (PDF, Reading Note)

  • Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding (PDF, Reading Note)

  • Learning to Segment Moving Objects in Videos (PDF, Reading Note)

  • Deep Structured Features for Semantic Segmentation (PDF)

    We propose a highly structured neural network architecture for semantic segmentation of images that combines i) a Haar wavelet-based tree-like convolutional neural network (CNN), ii) a random layer realizing a radial basis function kernel approximation, and iii) a linear classifier. While stages i) and ii) are completely pre-specified, only the linear classifier is learned from data. Thanks to its high degree of structure, our architecture has a very small memory footprint and thus fits onto low-power embedded and mobile platforms. We apply the proposed architecture to outdoor scene and aerial image semantic segmentation and show that the accuracy of our architecture is competitive with conventional pixel classification CNNs. Furthermore, we demonstrate that the proposed architecture is data efficient in the sense of matching the accuracy of pixel classification CNNs when trained on a much smaller data set.

  • CNN-aware Binary Map for General Semantic Segmentation (PDF)

  • Learning to Refine Object Segments (PDF)

  • Clockwork Convnets for Video Semantic Segmentation(PDF, Project/Code)

  • Convolutional Gated Recurrent Networks for Video Segmentation (PDF)

  • Efficient Convolutional Neural Network with Binary Quantization Layer (PDF)

  • One-Shot Video Object Segmentation (PDF)

  • Fully Convolutional Instance-aware Semantic Segmentation (PDF, Projcet/Code, Reading Note)

  • Semantic Segmentation using Adversarial Networks (PDF)

  • Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes (PDF)

  • Deep Watershed Transform for Instance Segmentation (PDF)

  • InstanceCut: from Edges to Instances with MultiCut (PDF)

  • The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation (PDF)

  • Improving Fully Convolution Network for Semantic Segmentation (PDF)

  • Video Scene Parsing with Predictive Feature Learning (PDF)

  • Training Bit Fully Convolutional Network for Fast Semantic Segmentation (PDF)

  • Pyramid Scene Parsing Network (PDF, Reading Note)

  • Mining Pixels: Weakly Supervised Semantic Segmentation Using Image Labels (PDF)

  • FastMask: Segment Object Multi-scale Candidates in One Shot (PDF, Project/Code, Reading Note)

  • A New Convolutional Network-in-Network Structure and Its Applications in Skin Detection, Semantic Segmentation, and Artifact Reduction (PDF, Reading Note)

  • FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos (PDF)

  • Visual Saliency Prediction Using a Mixture of Deep Neural Networks (PDF)

  • PixelNet: Representation of the pixels, by the pixels, and for the pixels (PDF, Project/Code)

  • Super-Trajectory for Video Segmentation (PDF)

  • Understanding Convolution for Semantic Segmentation (PDF, Reading Note)

  • Adversarial Examples for Semantic Image Segmentation (PDF)

  • Large Kernel Matters – Improve Semantic Segmentation by Global Convolutional Network (PDF)

  • Deep Image Matting (PDF, Reading Note)

  • Mask R-CNN (PDF, Caffe Implementation, TuSimple Implementation on MXNet, TensorFlow Implementation, Reading Note)

  • Predicting Deeper into the Future of Semantic Segmentation (PDF)

  • Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks (PDF, Project/Code)

  • One-Shot Video Object Segmentation (PDF, Project/Code)

  • Semantic Instance Segmentation via Deep Metric Learning (PDF)

  • Not All Pixels Are Equal: Difficulty-aware Semantic Segmentation via Deep Layer Cascade (PDF)

  • Semantically-Guided Video Object Segmentation (PDF)

  • Recurrent Multimodal Interaction for Referring Image Segmentation (PDF)

  • Loss Max-Pooling for Semantic Image Segmentation (PDF)

  • Reformulating Level Sets as Deep Recurrent Neural Network Approach to Semantic Segmentation (PDF)

  • Learning Video Object Segmentation with Visual Memory (PDF)

  • A Review on Deep Learning Techniques Applied to Semantic Segmentation (PDF)

  • BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks (PDF)

  • Rethinking Atrous Convolution for Semantic Image Segmentation (PDF)

  • Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules (PDF)

  • Superpixel-based semantic segmentation trained by statistical process control (PDF)

  • The Devil is in the Decoder (PDF)

  • Semantic Segmentation with Reverse Attention (PDF)

  • Learning Deconvolution Network for Semantic Segmentation (PDF, Project/Code)

  • Depth Adaptive Deep Neural Network for Semantic Segmentation (PDF)

  • Semantic Instance Segmentation with a Discriminative Loss Function (PDF)

  • A Cost-Sensitive Visual Question-Answer Framework for Mining a Deep And-OR Object Semantics from Web Images (PDF)

  • ICNet for Real-Time Semantic Segmentation on High-Resolution Images (PDF, Project/Code)

  • Pyramid Scene Parsing Network (PDF, Project/Code, Reading Note)

  • Learning to Segment Instances in Videos with Spatial Propagation Network (PDF, Project/Code)

  • Learning Affinity via Spatial Propagation Networks (PDF, Project/Code)

TRACKING

  • Tracking Objects as Points (PDF, Project/Code)
  • Deeper and Wider Siamese Networks for Real-Time Visual Tracking (PDF)
  • Multiple People Tracking Using Hierarchical Deep Tracklet Re-identification (PDF)
  • Fully-Convolutional Siamese Networks for Object Tracking (PDF)
  • Joint Flow: Temporal Flow Fields for Multi Person Tracking (PDF)
  • Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking (PDF)
  • Machine Learning Methods for Solving Assignment Problems in Multi-Target Tracking (PDF)
  • Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project (PDF)
  • Detect-and-Track: Efficient Pose Estimation in Videos (PDF)
  • Track, then Decide: Category-Agnostic Vision-based Multi-Object Tracking (PDF)
  • Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking (PDF, Reading Note)
  • Joint Tracking and Segmentation of Multiple Targets (PDF, Reading Note)
  • Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks (PDF)
  • Convolutional Regression for Visual Tracking (PDF)
  • Kernelized Correlation Filters(Project CODE1 CODE2)
  • Online Visual Multi-Object Tracking via Labeled Random Finite Set Filtering (PDF)
  • SANet: Structure-Aware Network for Visual Tracking (PDF)
  • Semantic tracking: Single-target tracking with inter-supervised convolutional networks (PDF)
  • On The Stability of Video Detection and Tracking (PDF)
  • Dual Deep Network for Visual Tracking (PDF)
  • Deep Motion Features for Visual Tracking (PDF)
  • Robust and Real-time Deep Tracking Via Multi-Scale Domain Adaptation (PDF, Project/Code)
  • Instance Flow Based Online Multiple Object Tracking (PDF)
  • PathTrack: Fast Trajectory Annotation with Path Supervision (PDF)
  • Good Features to Correlate for Visual Tracking (PDF)
  • Re3 : Real-Time Recurrent Regression Networks for Object Tracking (PDF)
  • Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning (PDF, Project/Code)
  • Simple Online and Realtime Tracking with a Deep Association Metric (PDF)
  • Learning Policies for Adaptive Tracking with Deep Feature Cascades (PDF)
  • Recurrent Filter Learning for Visual Tracking (PDF)
  • Tracking Persons-of-Interest via Unsupervised Representation Adaptation (PDF)
  • Detect to Track and Track to Detect (PDF, Project/Code, Reading Note)

POSE ESTIMATION

  • Human Pose Estimation with Spatial Contextual Information (PDF)
  • Rethinking on Multi-Stage Networks for Human Pose Estimation (PDF)
  • Learning to Estimate 3D Human Pose and Shape from a Single Color Image (PDF, Project/Code)
  • Ordinal Depth Supervision for 3D Human Pose Estimation (PDF, Project/Code)
  • Simple Baselines for Human Pose Estimation and Tracking (PDF)
  • End-to-end Recovery of Human Shape and Pose (PDF, PROJECT/CODE, Code)
  • PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model (PDF)
  • DensePose: Dense Human Pose Estimation In The Wild (PDF, Project/Code)
  • Cascaded Pyramid Network for Multi-Person Pose Estimation (PDF)
  • Chained Predictions Using Convolutional Neural Networks (PDF, Reading Note)
  • CRF-CNN: Modeling Structured Information in Human Pose Estimation (PDF)
  • Convolutional Pose Machines (PDF, Project/Code, Reading Note)
  • Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields (PDF, Project/Code, Reading Note)
  • Towards Accurate Multi-person Pose Estimation in the Wild (PDF, Reading Note)
  • Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation (PDF)
  • Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose (PDF, Project/Code)
  • Learning Feature Pyramids for Human Pose Estimation (PDF, Project/Code)
  • Joint Multi-Person Pose Estimation and Semantic Part Segmentation (PDF)
  • DeepPrior++: Improving Fast and Accurate 3D Hand Pose Estimation (PDF)
  • Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image (PDF)
  • Human Pose Regression by Combining Indirect Part Detection and Contextual Information (PDF)
  • Dual Path Networks for Multi-Person Human Pose Estimation (PDF)

ACTION RECOGNITION/EVENT DETECTION/VIDEO

  • Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition (PDF, Project/Code)
  • CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes (PDF, Project/Code, MxNet Version, Reading Note)
  • SlowFast Networks for Video Recognition (PDF)
  • PHD-GIFs: Personalized Highlight Detection for Automatic GIF Creation (PDF, Project/Code)
  • Superframes, A Temporal Video Segmentation (PDF)
  • Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation (PDF)
  • 2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning (PDF)
  • Real-Time End-to-End Action Detection with Two-Stream Networks (PDF)
  • Learning Video-Story Composition via Recurrent Neural Network (PDF)
  • Real-world Anomaly Detection in Surveillance Videos (PDF)
  • Fully-Coupled Two-Stream Spatiotemporal Networks for Extremely Low Resolution Action Recognition (PDF)
  • Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward (PDF, Project/Code)
  • Making a long story short: A Multi-Importance Semantic for Fast-Forwarding Egocentric Videos (PDF)
  • Attentional Pooling for Action Recognition (PDF, Project/Code)
  • Pooling the Convolutional Layers in Deep ConvNets for Action Recognition (PDF, Reading Note)
  • Two-Stream Convolutional Networks for Action Recognition in Videos (PDF, Reading Note)
  • YouTube-8M: A Large-Scale Video Classification Benchmark (PDF, Project/Code)
  • Spatiotemporal Residual Networks for Video Action Recognition (PDF)
  • An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data (PDF)
  • Fast Video Classification via Adaptive Cascading of Deep Models (PDF)
  • Video Pixel Networks (PDF)
  • Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection (PDF)
  • EM-Based Mixture Models Applied to Video Event Detection (PDF)
  • Video Captioning and Retrieval Models with Semantic Attention (PDF)
  • Title Generation for User Generated Videos (PDF)
  • Review of Action Recognition and Detection Methods (PDF)
  • RECURRENT MIXTURE DENSITY NETWORK FOR SPATIOTEMPORAL VISUAL ATTENTION (PDF)
  • Self-Supervised Video Representation Learning With Odd-One-Out Networks (PDF)
  • Recurrent Memory Addressing for describing videos (PDF)
  • Online Real time Multiple Spatiotemporal Action Localisation and Prediction on a Single Platform (PDF)
  • Real-Time Video Highlights for Yahoo Esports (PDF)
  • Surveillance Video Parsing with Single Frame Supervision (PDF)
  • Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks (PDF)
  • Action Recognition with Dynamic Image Networks (PDF)
  • ActionFlowNet: Learning Motion Representation for Action Recognition (PDF)
  • Video Propagation Networks (PDF)
  • Detecting events and key actors in multi-person videos (PDF)
  • A Pursuit of Temporal Accuracy in General Activity Detection (PDF, Reading Note)
  • Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos (PDF)
  • Deceiving Google’s Cloud Video Intelligence API Built for Summarizing Videos (PDF)
  • Incremental Tube Construction for Human Action Detection (PDF)
  • Unsupervised Action Proposal Ranking through Proposal Recombination (PDF)
  • CERN: Confidence-Energy Recurrent Network for Group Activity Recognition (PDF)
  • Forecasting Human Dynamics from Static Images (PDF)
  • Interpretable 3D Human Action Analysis with Temporal Convolutional Networks (PDF)
  • Training object class detectors with click supervision (PDF)
  • Skeleton-based Action Recognition with Convolutional Neural Networks (PDF)
  • Online growing neural gas for anomaly detection in changing surveillance scenes (PDF)
  • Learning Person Trajectory Representations for Team Activity Analysis (PDF)
  • Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition (PDF)
  • Video Imagination from a Single Image with Transformation Generation (PDF, Project/Code)
  • Optimizing Deep CNN-Based Queries over Video Streams at Scale (PDF, Project/Code, Reading Note)
  • Extreme Low Resolution Activity Recognition with Multi-Siamese Embedding Learning (PDF)
  • Predicting Human Activities Using Stochastic Grammar (PDF)
  • Discriminative convolutional Fisher vector network for action recognition (PDF)
  • Extreme Low Resolution Activity Recognition with Multi-Siamese Embedding Learning (PDF)
  • Exploiting Semantic Contextualization for Interpretation of Human Activity in Videos (PDF)
  • Lattice Long Short-Term Memory for Human Action Recognition (PDF)
  • Kinship Verification from Videos using Spatio-Temporal Texture Features and Deep Learning (PDF)
  • Fast-Forward Video Based on Semantic Extraction (PDF)
  • Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks (PDF)
  • ConvNet Architecture Search for Spatiotemporal Feature Learning (PDF, Project/Code, Github)
  • Fully Context-Aware Video Prediction (PDF)

FACE

  • BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs (PDF)
  • A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing (PDF, Project/Code)
  • Learning towards Minimum Hyperspherical Energy (PDF, Project/Code)
  • Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition (PDF, Code/Project)
  • Arbitrary Facial Attribute Editing: Only Change What You Want (PDF, Project/Code)
  • Anchor Cascade for Efficient Face Detection (PDF)
  • Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks (PDF, Reading Note)
  • MobileFaceNets: Efficient CNNs for Accurate Real-time Face Verification on Mobile Devices (PDF)
  • Survey of Face Detection on Low-quality Images (PDF)
  • PyramidBox: A Context-assisted Single Shot Face Detector (PDF)
  • SFace: An Efficient Network for Face Detection in Large Scale Variations ([PDF](SFace: An Efficient Network for Face Detection in Large Scale Variations))
  • Deep Facial Expression Recognition: A Survey (PDF)
  • Deep Face Recognition: A Survey (PDF)
  • Deep Semantic Face Deblurring (PDF, Project/Code)
  • Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild (PDF)
  • SSH: Single Stage Headless Face Detector (PDF, Project/Code)
  • Detecting and counting tiny faces (PDF, Project/Code)
  • Training Deep Face Recognition Systems with Synthetic Data (PDF)
  • Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (PDF, Project/Code)
  • Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks (PDF, Project/Code, Code Caffe)
  • Deep Architectures for Face Attributes (PDF)
  • Face Detection with End-to-End Integration of a ConvNet and a 3D Model (PDF, Reading Note, Project/Code)
  • A CNN Cascade for Landmark Guided Semantic Part Segmentation (PDF, Project/Code)
  • Kernel Selection using Multiple Kernel Learning and Domain Adaptation in Reproducing Kernel Hilbert Space, for Face Recognition under Surveillance Scenario (PDF)
  • An All-In-One Convolutional Neural Network for Face Analysis (PDF)
  • Fast Face-swap Using Convolutional Neural Networks (PDF)
  • Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval (Project/Code)
  • CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection (Project/Code)
  • Face Synthesis from Facial Identity Features (PDF)
  • DeepFace: Face Generation using Deep Learning (PDF)
  • Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns (PDF, Project/Code)
  • EmotioNet Challenge: Recognition of facial expressions of emotion in the wild (PDF)
  • Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation (PDF)
  • Semi and Weakly Supervised Semantic Segmentation Using Generative Adversarial Network (PDF)
  • Deep Alignment Network: A convolutional neural network for robust face alignment (PDF, Project/Code)
  • Scale-Aware Face Detection (PDF)
  • SSH: Single Stage Headless Face Detector (PDF)
  • AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild (PDF)
  • SphereFace: Deep Hypersphere Embedding for Face Recognition (PDF, Project/Code)
  • Age Group and Gender Estimation in the Wild with Deep RoR Architecture (PDF)
  • Island Loss for Learning Discriminative Features in Facial Expression Recognition (PDF)
  • Temporal Non-Volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition (PDF)

OPTICAL FLOW

  • LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation (PDF, Project/Code)
  • DeepFlow: Large displacement optical flow with deep matching (PDF, Project/Code)
  • Guided Optical Flow Learning (PDF)

IMAGE PROCESSING

  • R2D2: Repeatable and Reliable Detector and Descriptor (PDF)
  • CartoonGAN: Generative Adversarial Networks for Photo Cartoonization (PDF)
  • Image Inpainting for Irregular Holes Using Partial Convolutions (PDF)
  • Neural Aesthetic Image Reviewer (PDF, Reading Note)
  • Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression (PDF)
  • Learning Intelligent Dialogs for Bounding Box Annotation (PDF)
  • Real-time video stabilization and mosaicking for monitoring and surveillance (PDF, Project/Code)
  • Learning Recursive Filter for Low-Level Vision via a Hybrid Neural Network (PDF, Project/Code)
  • Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding(PDF, Project/Code)
  • A Learned Representation For Artistic Style(PDF)
  • Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification (PDF, Project/Code)
  • Pixel Recurrent Neural Networks (PDF)
  • Conditional Image Generation with PixelCNN Decoders (PDF, Project/Code)
  • RAISR: Rapid and Accurate Image Super Resolution (PDF)
  • Photo-Quality Evaluation based on Computational Aesthetics: Review of Feature Extraction Techniques (PDF)
  • Fast color transfer from multiple images (PDF)
  • Bringing Impressionism to Life with Neural Style Transfer in Come Swim (PDF)
  • PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications (PDF, (Project/CODE)[https://github.com/openai/pixel-cnn])
  • Deep Photo Style Transfer (PDF)
  • A Neural Representation of Sketch Drawings (PDF)
  • Visual Attribute Transfer through Deep Image Analogy (PDF)
  • Deep Semantics-Aware Photo Adjustment (PDF)
  • Diversified Texture Synthesis with Feed-forward Networks (PDF, Project/Code)
  • Real-Time Neural Style Transfer for Videos (PDF)
  • Creatism: A deep-learning photographer capable of creating professional work (PDF)
  • Deep Image Harmonization (PDF, Project/Code)
  • Neural Color Transfer between Images (PDF)
  • Deeper, Broader and Artier Domain Generalization (PDF)

3D/DEPTH/POINT CLOUD

  • The Perfect Match: 3D Point Cloud Matching with Smoothed Densities (PDF, Project/Code)
  • Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling (PDF, Project/Code)
  • Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras (PDF)

CNN AND DEEP LEARNING

  • ResNeSt: Split-Attention Networks (PDF, Project/Code, Reading Note)

  • Meta-Learning in Neural Networks: A Survey (PDF, )

  • Generalizing from a Few Examples: A Survey on Few-Shot Learning (PDF)

  • NBDT: Neural-Backed Decision Trees (PDF, Project/Code, Github, Reading Note)

  • Interpretable CNNs (PDF)

  • Bag of Tricks for Image Classification with Convolutional Neural Networks (PDF)

  • How Does Batch Normalization Help Optimization? (PDF, VIDEO)

  • https://arxiv.org/abs/1805.07883 (PDF)

  • Rethinking ImageNet Pre-training (PDF)

  • Learning From Positive and Unlabeled Data: A Survey (PDF)

  • Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks (PDF, Project/Code)

  • DropBlock: A regularization method for convolutional networks (PDF)

  • Differentiable Abstract Interpretation for Provably Robust Neural Networks (PDF, Project/Code)

  • Adding One Neuron Can Eliminate All Bad Local Minima (PDF)

  • Step Size Matters in Deep Learning (PDF)

  • Do Better ImageNet Models Transfer Better? (PDF)

  • Robust Classification with Convolutional Prototype Learning (PDF, Project/Code)

  • Fast Feature Extraction with CNNs with Pooling Layers (PDF)

  • Network Transplanting (PDF)

  • An Information-Theoretic View for Deep Learning (PDF)

  • Understanding Individual Neuron Importance Using Information Theory (PDF)

  • Understanding Convolutional Neural Network Training with Information Theory (PDF)

  • The unreasonable effectiveness of the forget gate (PDF)

  • Discovering Hidden Factors of Variation in Deep Networks (PDF)

  • Regularizing Deep Networks by Modeling and Predicting Label Structure (PDF)

  • Hierarchical Novelty Detection for Visual Object Recognition (PDF)

  • Guide Me: Interacting with Deep Networks (PDF)

  • Studying Invariances of Trained Convolutional Neural Networks (PDF)

  • Deep Residual Networks and Weight Initialization (PDF)

  • WNGrad: Learn the Learning Rate in Gradient Descent (PDF)

  • Understanding the Loss Surface of Neural Networks for Binary Classification (PDF)

  • Tell Me Where to Look: Guided Attention Inference Network (PDF)

  • Convolutional Neural Networks with Alternately Updated Clique (PDF, Project/Code)

  • Visual Interpretability for Deep Learning: a Survey (PDF)

  • Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey (PDF)

  • CNNs are Globally Optimal Given Multi-Layer Support (PDF)

  • Take it in your stride: Do we need striding in CNNs? (PDF)

  • Gradients explode - Deep Networks are shallow - ResNet explained (PDF)

  • Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates (PDF, Project/Code)

  • Data Distillation: Towards Omni-Supervised Learning (PDF)

  • Peephole: Predicting Network Performance Before Training (PDF)

  • AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks (PDF)

  • Gradual Tuning: a better way of Fine Tuning the parameters of a Deep Neural Network (PDF)

  • CondenseNet: An Efficient DenseNet using Learned Group Convolutions (PDF, Project/Code)

  • Population Based Training of Neural Networks (PDF)

  • Knowledge Concentration: Learning 100K Object Classifiers in a Single CNN (PDF)

  • Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions (PDF)

  • Unleashing the Potential of CNNs for Interpretable Few-Shot Learning (PDF)

  • Non-local Neural Networks (PDF, Caffe2)

  • Log-DenseNet: How to Sparsify a DenseNet (PDF)

  • Don’t Decay the Learning Rate, Increase the Batch Size (PDF)

  • Guarding Against Adversarial Domain Shifts with Counterfactual Regularization (PDF)

  • UberNet: Training a ‘Universal’ Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory (PDF, Project/Code)

  • What makes ImageNet good for transfer learning? (PDF, Project/Code, Reading Note)

    The tremendous success of features learnt using the ImageNet classification task on a wide range of transfer tasks begs the question: what are the intrinsic properties of the ImageNet dataset that are critical for learning good, general-purpose features? This work provides an empirical investigation of various facets of this question: Is more pre-training data always better? How does feature quality depend on the number of training examples per class? Does adding more object classes improve performance? For the same data budget, how should the data be split into classes? Is fine-grained recognition necessary for learning good features? Given the same number of training classes, is it better to have coarse classes or fine-grained classes? Which is better: more classes or more examples per class?

  • Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units (PDF)

  • Densely Connected Convolutional Networks (PDF, Project/Code, Reading Note)

  • Decoupled Neural Interfaces using Synthetic Gradients (PDF)

    Training directed neural networks typically requires forward-propagating data through a computation graph, followed by backpropagating error signal, to produce weight updates. All layers, or more generally, modules, of the network are therefore locked, in the sense that they must wait for the remainder of the network to execute forwards and propagate error backwards before they can be updated. In this work we break this constraint by decoupling modules by introducing a model of the future computation of the network graph. These models predict what the result of the modeled sub-graph will produce using only local information. In particular we focus on modeling error gradients: by using the modeled synthetic gradient in place of true backpropagated error gradients we decouple subgraphs, and can update them independently and asynchronously.

  • Rethinking the Inception Architecture for Computer Vision (PDF, Reading Note)

    In this paper, several network designing choices are discussed, including factorizing convolutions into smaller kernels and asymmetric kernels, utility of auxiliary classifiers and reducing grid size using convolution stride rather than pooling.

  • Factorized Convolutional Neural Networks (PDF, Reading Note)

  • Do semantic parts emerge in Convolutional Neural Networks? (PDF, Reading Note)

  • A Critical Review of Recurrent Neural Networks for Sequence Learning (PDF)

  • Image Compression with Neural Networks (Project/Code)

  • Graph Convolutional Networks (Project/Code)

  • Understanding intermediate layers using linear classifier probes (PDF, Reading Note)

  • Learning What and Where to Draw (PDF, Project/Code)

  • On the interplay of network structure and gradient convergence in deep learning (PDF)

  • Deep Learning with Separable Convolutions (PDF)

  • Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization (PDF, Project/Code)

  • Optimization of Convolutional Neural Network using Microcanonical Annealing Algorithm (PDF)

  • Deep Pyramidal Residual Networks (PDF)

  • Impatient DNNs - Deep Neural Networks with Dynamic Time Budgets (PDF)

  • Uncertainty in Deep Learning (PDF, Project/Code)
    This is the PhD Thesis of Yarin Gal.

  • Tensorial Mixture Models (PDF, Project/Code)

  • Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks (PDF)

  • Why Deep Neural Networks? (PDF)

  • Local Similarity-Aware Deep Feature Embedding (PDF)

  • A Review of 40 Years of Cognitive Architecture Research: Focus on Perception, Attention, Learning and Applications (PDF)

  • Professor Forcing: A New Algorithm for Training Recurrent Networks (PDF)

  • On the expressive power of deep neural networks(PDF)

  • What Is the Best Practice for CNNs Applied to Visual Instance Retrieval? (PDF)

  • Deep Convolutional Neural Network Design Patterns (PDF, Project/Code)

  • Tricks from Deep Learning (PDF)

  • A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models (PDF)

  • Multi-Shot Mining Semantic Part Concepts in CNNs (PDF)

  • Aggregated Residual Transformations for Deep Neural Networks (PDF, Reading Note)

  • PolyNet: A Pursuit of Structural Diversity in Very Deep Networks (PDF)

  • On the Exploration of Convolutional Fusion Networks for Visual Recognition (PDF)

  • ResFeats: Residual Network Based Features for Image Classification (PDF)

  • Object Recognition with and without Objects (PDF)

  • LCNN: Lookup-based Convolutional Neural Network (PDF, Reading Note)

  • Inductive Bias of Deep Convolutional Networks through Pooling Geometry (PDF, Project/Code)

  • Wider or Deeper: Revisiting the ResNet Model for Visual Recognition (PDF, Reading Note)

  • Multi-Scale Context Aggregation by Dilated Convolutions (PDF, Project/Code)

  • Large-Margin Softmax Loss for Convolutional Neural Networks (PDF, mxnet Code, Caffe Code)

  • Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics (PDF)

  • Feedback Networks (PDF)

  • Visualizing Residual Networks (PDF)

  • Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks (PDF, Project/Code)

  • Understanding trained CNNs by indexing neuron selectivity (PDF)

  • Benchmarking State-of-the-Art Deep Learning Software Tools (PDF, Project/Code)

  • Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models (PDF)

  • Visualizing Deep Neural Network Decisions: Prediction Difference Analysis (PDF, Project/Code)

  • ShaResNet: reducing residual network parameter number by sharing weights (PDF)

  • Deep Forest: Towards An Alternative to Deep Neural Networks (PDF, Project/Code)

  • All You Need is Beyond a Good Init: Exploring Better Solution for Training Extremely Deep Convolutional Neural Networks with Orthonormality and Modulation (PDF)

  • Genetic CNN (PDF)

  • Deformable Convolutional Networks (PDF)

  • Quality Resilient Deep Neural Networks (PDF)

  • How ConvNets model Non-linear Transformations (PDF)

  • Active Convolution: Learning the Shape of Convolution for Image Classification (PDF)

  • Multi-Scale Dense Convolutional Networks for Efficient Prediction (PDF, Project/Code)

  • Coordinating Filters for Faster Deep Neural Networks (PDF, Project/Code)

  • A Genetic Programming Approach to Designing Convolutional Neural Network Architectures (PDF)

  • On Generalization and Regularization in Deep Learning (PDF)

  • Interpretable Explanations of Black Boxes by Meaningful Perturbation (PDF)

  • Energy Propagation in Deep Convolutional Neural Networks (PDF)

  • Introspection: Accelerating Neural Network Training By Learning Weight Evolution (PDF)

  • Deeply-Supervised Nets (PDF)

  • Speeding up Convolutional Neural Networks By Exploiting the Sparsity of Rectifier Units (PDF)

  • Inception Recurrent Convolutional Neural Network for Object Recognition (PDF)

  • Residual Attention Network for Image Classification (PDF)

  • The Landscape of Deep Learning Algorithms (PDF)

  • Pixel Deconvolutional Networks (PDF)

  • Dilated Residual Networks (PDF)

  • A Kernel Redundancy Removing Policy for Convolutional Neural Network (PDF)

  • Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour (PDF)

  • Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification (PDF, Project/Code, Reading Note)

  • VisualBackProp: efficient visualization of CNNs (PDF)

  • Pruning Convolutional Neural Networks for Resource Efficient Inference (PDF, Project/Code)

  • Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly (PDF, Project/Code)

  • ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices (PDF, Caffe Implementation)

  • Submanifold Sparse Convolutional Networks (PDF, Project/Code)

  • Dual Path Networks (PDF)

  • ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression (PDF, Project/Code, Reading Note)

  • Memory-Efficient Implementation of DenseNets (PDF)

  • Residual Attention Network for Image Classification (PDF, Project/Code)

  • An Effective Training Method For Deep Convolutional Neural Network (PDF)

  • Learning to Transfer (PDF)

  • Learning Efficient Convolutional Networks through Network Slimming (PDF, Project/Code)

  • Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates (PDF, Project/Code)

  • Hierarchical loss for classification (PDF)

  • Convolutional Gaussian Processes (PDF, Code/Project)

  • Interpretable Convolutional Neural Networks (PDF)

  • What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? (PDF)

  • Porcupine Neural Networks: (Almost) All Local Optima are Global (PDF)

  • Generalization in Deep Learning (PDF)

  • A systematic study of the class imbalance problem in convolutional neural networks (PDF)

  • Interpretable Transformations with Encoder-Decoder Networks (PDF, Project/Code)

  • One pixel attack for fooling deep neural networks (PDF)

SINGLE-SHOT/UNSUPERVISED LEARNING

  • Zero-Shot Object Detection by Hybrid Region Embedding (PDF, Project/Code)
  • Deep Triplet Ranking Networks for One-Shot Recognition (PDF)
  • Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration (PDF)

GAN

  • A Survey on GANs for Anomaly Detection (PDF)
  • Outfit Generation and Style Extraction via Bidirectional LSTM and Autoencoder (PDF)
  • Pioneer Networks: Progressively Growing Generative Autoencoder (PDF)
  • Transferring GANs: generating images from limited data (PDF, Project/Code)
  • Painting Generation Using Conditional Generative Adversarial Net (PDF, Project/Code)
  • MGGAN: Solving Mode Collapse using Manifold Guided Training (PDF)
  • Multimodal Unsupervised Image-to-Image Translation (PDF, Project/Code)
  • Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond (PDF)
  • Face Aging with Contextual Generative Adversarial Nets (PDF, Project/Code)
  • Deformable GANs for Pose-based Human Image Generation (PDF, Project/Code)
  • ComboGAN: Unrestrained Scalability for Image Domain Translation (PDF, Project/Code)
  • Eye In-Painting with Exemplar Generative Adversarial Networks (PDF)
  • Disentangled Person Image Generation (PDF)
  • Fader Networks: Manipulating Images by Sliding Attributes (PDF, Code/Project)
  • Are GANs Created Equal? A Large-Scale Study (PDF)
  • StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation (PDF, Project/Code)
  • Two Birds with One Stone: Iteratively Learn Facial Attributes with GANs (PDF, Project/Code)
  • Spectral Normalization for Generative Adversarial Networks (PDF)
  • XGAN: Unsupervised Image-to-Image Translation for many-to-many Mappings (PDF)
  • How Generative Adversarial Nets and its variants Work: An Overview of GAN (PDF)
  • DNA-GAN: Learning Disentangled Representations from Multi-Attribute Images (PDF, Project/Code)
  • Sobolev GAN (PDF)
  • Data Augmentation Generative Adversarial Networks (PDF)
  • Conditional Autoencoders with Adversarial Information Factorization (PDF, Project/Code)
  • Progressive Growing of GANs for Improved Quality, Stability, and Variation (PDF, Project/Code, Torch, PyTorch, Reading Note)
  • Bayesian GAN (PDF, Project/Code)
  • Metric Learning-based Generative Adversarial Network (PDF)
  • Flexible Prior Distributions for Deep Generative Models (PDF)
  • Data Augmentation in Classification using GAN (PDF)
  • Semantically Decomposing the Latent Spaces of Generative Adversarial Networks (PDF)
  • Multi-View Data Generation Without View Supervision (PDF)
  • StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks (PDF)
  • Generative Adversarial Networks (PDF)
  • Stacked Generative Adversarial Networks (PDF)
  • Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks (PDF)
  • Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (PDF)
  • Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks (PDF)
  • NIPS 2016 Tutorial: Generative Adversarial Networks (PDF)
  • Wasserstein GAN (PDF)
  • Adversarial Discriminative Domain Adaptation (PDF, Reading Note)
  • Generative Adversarial Nets with Labeled Data by Activation Maximization (PDF)
  • Triple Generative Adversarial Nets (PDF)
  • On the Quantative Evaluation of Deep Generative Models (PDF)
  • Adversarial Transformation Networks: Learning to Generate Adversarial Examples (PDF)
  • Improved Training of Wasserstein GANs (PDF, Project/Code)
  • Generate To Adapt: Aligning Domains using Generative Adversarial Networks (PDF)
  • Adversarial Generator-Encoder Networks (PDF, Project/Code)
  • Training Triplet Networks with GAN (PDF)
  • Multi-Agent Diverse Generative Adversarial Networks (PDF)
  • GP-GAN: Towards Realistic High-Resolution Image Blending (PDF, Project/Code)
  • BEGAN: Boundary Equilibrium Generative Adversarial Networks (PDF)
  • MAGAN: Margin Adaptation for Generative Adversarial Networks (PDF)
  • Pose Guided Person Image Generation (PDF)
  • On the Effects of Batch and Weight Normalization in Generative Adversarial Networks (PDF, Project/Code)
  • Aesthetic-Driven Image Enhancement by Adversarial Learning (PDF)
  • VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning (PDF, Project/Code
  • MoCoGAN: Decomposing Motion and Content for Video Generation (PDF, Project/Code)
  • Generative Adversarial Networks: An Overview ((PDF)[https://arxiv.org/abs/1710.07035])
  • SalGAN: Visual Saliency Prediction with Generative Adversarial Networks (PDF, Project/Code)

MACHINE LEARNING

LIGHT-WEIGHT MODEL/EMBEDDED/MOBILE/MODEL COMPRESSION

  • MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning (PDF, Project/Code)
  • PyTorch Network Slimming (PDF, Project/Code)
  • Importance Estimation for Neural Network Pruning (PDF, Project/Code)
  • MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning (PDF)
  • EFFICIENT METHODS AND HARDWARE FOR DEEP LEARNING (PDF)
  • ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware (PDF, Project/Code)
  • FD-MobileNet: Improved MobileNet with a Fast Downsampling Strategy (PDF)
  • Quantization Mimic: Towards Very Tiny CNN for Object Detection (PDF)
  • Pelee: A Real-Time Object Detection System on Mobile Devices (PDF, Project/Code, TensorRT Implemented, Reading Note)
  • MobileNetV2: Inverted Residuals and Linear Bottlenecks (PDF, Reading Note)
  • SBNet: Sparse Blocks Network for Fast Inference (PDF, Project/Code)
  • IGCV2: Interleaved Structured Sparse Convolutional Neural Networks (PDF)
  • FitNets: Hints for Thin Deep Nets (PDF)
  • Building Efficient ConvNets using Redundant Feature Pruning (PDF, Project/Code)
  • Multi-Scale Dense Networks for Resource Efficient Image Classification (PDF)
  • Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee (pdf)
  • NISP: Pruning Networks using Neuron Importance Score Propagation (PDF)
  • Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks (PDF)
  • Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs (PDF)
  • FINN: A Framework for Fast, Scalable Binarized Neural Network Inference (PDF)
  • Two-Bit Networks for Deep Learning on Resource-Constrained Embedded Devices (PDF)
  • SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size (PDF, Project/Code)
  • MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (PDF, Caffe Implementation, Reading Note)
  • Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration (PDF)
  • Channel Pruning for Accelerating Very Deep Neural Networks (PDF, Project/Code)
  • Quantized Convolutional Neural Networks for Mobile Devices (PDF, Project/Code)
  • Squeeze-and-Excitation Networks (PDF)
  • Domain-adaptive deep network compression (PDF)
  • Embedded Binarized Neural Networks (PDF)
  • Keynote: Small Neural Nets Are Beautiful: Enabling Embedded Systems with Small Deep-Neural-Network Architectures (PDF)
  • A Survey of Model Compression and Acceleration for Deep Neural Networks ([https://arxiv.org/abs/1710.09282])

ReID

  • Video-based Person Re-identification via 3D Convolutional Networks and Non-local Attention (PDF)
  • Attention-Aware Compositional Network for Person Re-identification (PDF)
  • Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification (PDF, Project/Code)
  • Features for Multi-Target Multi-Camera Tracking and Re-Identification (PDF)
  • Video Person Re-identification by Temporal Residual Learning (PDF)
  • Harmonious Attention Network for Person Re-Identification (PDF)
  • In Defense of the Triplet Loss for Person Re-Identification (PDF)
  • Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-Free Approach (PDF)
  • AlignedReID: Surpassing Human-Level Performance in Person Re-Identification (PDF)
  • A Discriminatively Learned CNN Embedding for Person Re-identification (PDF, Project/Code)
  • Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-temporal Path Proposals (PDF)
  • Beyond triplet loss: a deep quadruplet network for person re-identification (PDF)
  • Person Re-identification by Local Maximal Occurrence Representation and Metric Learning (PDF, Project/Code)
  • Person Re-identification: Past, Present and Future (PDF)
  • Unsupervised Person Re-identification: Clustering and Fine-tuning (PDF, Project/Code)
  • Jointly Attentive Spatial-Temporal Pooling Networks for Video-based Person Re-Identification (PDF)
  • Divide and Fuse: A Re-ranking Approach for Person Re-identification (PDF)
  • Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification (PDF)
  • HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis (PDF, Project/Code)

FASHION

  • Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba (PDF)
  • Visually-Aware Fashion Recommendation and Design with Generative Image Models ([PDF](Visually-Aware Fashion Recommendation and Design with Generative Image Models))
  • Be Your Own Prada: Fashion Synthesis with Structural Coherence (PDF, Project/Code, Reading Note)
  • Style2Vec: Representation Learning for Fashion Items from Style Sets (PDF)
  • Dress like a Star: Retrieving Fashion Products from Videos (PDF)
  • The Conditional Analogy GAN: Swapping Fashion Articles on People Images (PDF)

OTHER

  • GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition (PDF, Project/Code)
  • Deep Clustering for Unsupervised Learning of Visual Features (PDF)
  • Detecting Visual Relationships Using Box Attention (PDF)
  • Zoom-Net: Mining Deep Feature Interactions for Visual Relationship Recognition (PDF, Project/Code)
  • Learning to See in the Dark(PDF)
  • A Variational U-Net for Conditional Appearance and Shape Generation (PDF, Project/Code)
  • Synthesizing Images of Humans in Unseen Poses (PDF)
  • End-to-end weakly-supervised semantic alignment (PDF, Project/Code)
  • Dense Optical Flow based Change Detection Network Robust to Difference of Camera Viewpoints (PDF)
  • Dual-Path Convolutional Image-Text Embedding (PDF, Project/Code)
  • The Promise and Peril of Human Evaluation for Model Interpretability (PDF)
  • Semantic Image Retrieval via Active Grounding of Visual Situations (PDF)
  • LIFT: Learned Invariant Feature Transform (PDF)
  • Learning Aligned Cross-Modal Representations from Weakly Aligned Data (PDF, Project/Code)
  • Multi-Task Curriculum Transfer Deep Learning of Clothing Attributes (PDF)
  • End-to-end Learning of Deep Visual Representations for Image Retrieval (PDF)
  • SoundNet: Learning Sound Representations from Unlabeled Video (PDF)
  • Bags of Local Convolutional Features for Scalable Instance Search (PDF, Project/Code)
  • Universal Correspondence Network (PDF, Project/Code)
  • Judging a Book By its Cover (PDF)
  • Generalisation and Sharing in Triplet Convnets for Sketch based Visual Search (PDF)
  • Analysis and Optimization of Loss Functions for Multiclass, Top-k, and Multilabel Classification (PDF)
  • Automatic generation of large-scale handwriting fonts via style learning (PDF)
  • Image Retrieval with Deep Local Features and Attention-based Keypoints (PDF)
  • Visual Discovery at Pinterest (PDF)
  • Learning to Detect Human-Object Interactions (PDF, Project/Code, Reading Note)
  • Learning Deep Features via Congenerous Cosine Loss for Person Recognition (PDF)
  • Large-Scale Evolution of Image Classifiers (PDF)
  • Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection (PDF)
  • Twitter100k: A Real-world Dataset for Weakly Supervised Cross-Media Retrieval (PDF, Project/Code)
  • Mixture of Counting CNNs: Adaptive Integration of CNNs Specialized to Specific Appearance for Crowd Counting (PDF)
  • Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (PDF, Project/Code)
  • Learning Features by Watching Objects Move (PDF, Project/Code)
  • GMS: Grid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence (PDF, Project/Code)
  • ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification (PDF)
  • Learning Cross-modal Embeddings for Cooking Recipes and Food Images (PDF, Project/Code)
  • Convolutional neural network architecture for geometric matching (PDF, Project/Code)
  • Semantic Compositional Networks for Visual Captioning (PDF, Project/Code)
  • CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting (PDF)
  • Understanding Black-box Predictions via Influence Functions (PDF)
  • Learning a Repression Network for Precise Vehicle Search (PDF)
  • Visual Graph Mining (PDF)
  • A Deep Multimodal Approach for Cold-start Music Recommendation (PDF)
  • A Multilayer-Based Framework for Online Background Subtraction with Freely Moving Cameras (PDF)
  • A self-organizing neural network architecture for learning human-object interactions (PDF)

INTERESTING FINDS

RESOURCES/PERSPECTIVES

PROJECTS

NEWS/BLOGS

BENCHMARK/LEADERBOARD/DATASET

TOOLKITS

  • XGBoostLSS An extension of XGBoost to probabilistic forecasting

  • Netron is a viewer for neural network, deep learning and machine learning models.

  • Bring Deep Learning to small devices An open source deep learning platform for low bit computation

  • Albumentations fast image augmentation library and easy to use wrapper around other libraries.

  • FeatherCNN
    FeatherCNN is a high performance inference engine for convolutional neural networks.

  • Caffe
    Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license.

  • Caffe2
    Caffe2 is a deep learning framework made with expression, speed, and modularity in mind. It is an experimental refactoring of Caffe, and allows a more flexible way to organize computation.

  • Caffe on Intel
    This fork of BVLC/Caffe is dedicated to improving performance of this deep learning framework when running on CPU, in particular Intel® Xeon processors (HSW+) and Intel® Xeon Phi processors

  • TensorFlow
    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit.

  • MXNet
    MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix the flavours of symbolic programming and imperative programming to maximize efficiency and productivity. In its core, a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. The library is portable and lightweight, and it scales to multiple GPUs and multiple machines.

  • neon
    neon is Nervana’s Python based Deep Learning framework and achieves the fastest performance on modern deep neural networks such as AlexNet, VGG and GoogLeNet. Designed for ease-of-use and extensibility.

  • Piotr’s Computer Vision Matlab Toolbox
    This toolbox is meant to facilitate the manipulation of images and video in Matlab. Its purpose is to complement, not replace, Matlab’s Image Processing Toolbox, and in fact it requires that the Matlab Image Toolbox be installed. Emphasis has been placed on code efficiency and code reuse. Thanks to everyone who has given me feedback - you’ve helped make this toolbox more useful and easier to use.

  • NVIDIA Developer

  • nvCaffe
    A special branch of caffe is used on TX1 which includes support for FP16.

  • dlib
    Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. Dlib’s open source licensing allows you to use it in any application, free of charge.

  • OpenCV
    OpenCV is released under a BSD license and hence it’s free for both academic and commercial use. It has C++, C, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android. OpenCV was designed for computational efficiency and with a strong focus on real-time applications.

  • CNNdroid
    CNNdroid is an open source library for execution of trained convolutional neural networks on Android devices.

  • tiny dnn
    tiny-dnn is a C++11 implementation of deep learning. It is suitable for deep learning on limited computational resource, embedded systems and IoT devices.

    An introduction to this toolkit at《Deep learning with C++ - an introduction to tiny-dnn》by Taiga Nomi

  • CaffeMex
    A multi-GPU & memory-reduced MAT-Caffe on LINUX and WINDOWS

  • ARCore ARCore is a platform for building augmented reality apps on Android. ARCore uses three key technologies to integrate virtual content with the real world as seen through your phone’s camera

  • CNTK Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit.

  • ONNX ONNX is a open format to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools and choose the combination that is best for them. ONNX is developed and supported by a community of partners.

  • PyToune is a Keras-like framework for PyTorch and handles much of the boilerplating code needed to train neural networks.

  • Deep Learning Studio - Desktop DeepCognition.ai is a single user solution that runs locally on your hardware. Desktop version allows you to train models on your GPU(s) without uploading data to the cloud. The platform supports transparent multi-GPU training for up to 4 GPUs. Additional GPUs are supported in Deep Learning Studio – Enterprise.

LEARNING/TRICKS/TIPS

SKILLS

ABOUT CAFFE

SETTING UP

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TITLE: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

AUTHER: Adam Paszke, Abhishek Chaurasia, Sangpil Kim, Eugenio Culurciello

ASSOCIATION: University of Warsaw, Purdue University

FROM: arXiv:1606.02147

CONTRIBUTIONS

  1. A novel deep neural network architecture named ENet (efficient neural network) is propsed, which is quite efficient.
  2. A serie of designing strategies is discussed.

Design Choices

Network Architecture

Readers could refer to the paper to have a look at the network architecture. The network is inspired by ResNet structure, while the authers re-design it based on the specific task of semantic segmentation and their intuitions. The intial block and basic building block (bottlenect module) is shown in the following figure. After the intial block, a comparetively large encoder is constructed using the bottleneck module. On the other hand, a smaller decoder follows the encoder.

Design Strategy

  1. Feature map resolution: Small feature map resolution has two drawbacks 1) loss of finer information of edges and 2) smaller size compared with original image. The advantage is that small feature map resolution means larger receptive field and more context for the filters. The first problem is solved by adding more feature maps or unsampling technique.
  2. Early downsampling: Early downsampling is very helpful for boosting the efficiency of the network while persisting the performance. The idea is that visual information is highly redundant and that initial network layers should not directly contribute to classification but act as good feature extractors.
  3. Decoder size: In most previous works, the encoder and decoder have the same size, for example totally symmetric. In this work, the auther uses a larger encoder and a smaller decoder. The responsibility of encoder is to operate on smaller resolution data and provide for information processing and filtering. Instead, the role of the the decoder, is to upsample the output of the encoder, only fine-tuning the details.
  4. Nonlinear operations In this paper some interesting observations are carried out. The auther invetigates the effect of nonlinear operations by training the network using PReLU. All layers in the main branch behave nearly exactly like regular ReLUs, while the weights of PReLU inside bottleneck modules are negative. It means that typical identity shortcut in ResNet does not work well because of the limited depth of the network.
  5. Information-preserving dimensionality changes: A method of performing pooling operation in parallel with a convolution of stride 2 and concatenating resulting feature maps is used to guarentee efficiency and performance, just as the intial block shows.
  6. Factorizing filters: Using factorizing technique can achive a kernel of larger size while using less computations. In addition, deeper network and more times of non-linear operation helps simulate richer functions.
  7. Dilated convolutions: Dilated convolutions is a good way of maintaining feature resolution while boosting efficiency.
  8. Regularization: Spatial Dropout is used to prevent overfitting.

ADVANTAGES

  1. The network processes fast.

DISADVANTAGES

  1. The performance is comparatively inferior.

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TITLE: Factorized Convolutional Neural Networks

AUTHER: Min Wang, Baoyuan Liu, Hassan Foroosh

ASSOCIATION: Department of EECS, University of Central Florida, Orlando

FROM: arXiv:1608.04337

CONTRIBUTIONS

  1. A new implementation of convolutional layer is proposed and only involves single in-channel convolution and linear channel projection.
  2. The network using such layers can achieves similar accuracy with significantly less computaion.

METHOD

Convolutional Layer with Bases

When $b = k^2$, this layer is equivalent to the standard convolutional layer. The number of multiplication required for this layer is $hwbm(k^2 + n)$, which means that by reducing b and increasing k, we create a layer that achieves large convolutional kernel while maintaining low complexity.

Convolutional Layer as Stacked Single Basis Layer

One assumption is that the number of output channels is the same as the number of input channels $m = n$, which is the case of that in ResNet. The modified layer can be considered as stacking multiple convolutional layers with single basis. Residual learning is also introduced in thie modified layer, which solves the problem of losing useful information caused by single basis.

Topological Connections

A $n$-dimensional topological connections between the input and output channels in convolutional layer is proposed. Each output channel is only connected with its local neighbors rather than all input channels.

ADVANTAGES

  1. It is an interesting method of speeding up CNN as the auther claims that the network achieves accuracy of GoogLeNet while consuming 3.4 times less computaion.

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最近没有怎么看书学习，倒是看了一部2007年上映的日本TV动画——《精灵守护者》，改编自上桥菜穗子的儿童系列小说《守护人·旅人》中的第一部《精灵之守护人》，动画一共26话。作品是世界观很有意思，这个架空世界里有分为两个世界，肉眼所能看到的人类的世界及肉眼看不到的精灵的世界，这两个世界的存在很像平行世界，在特定条件下可以相互产生作用。

在世界观设定中，精灵世界里的水精灵一百年产一次卵，新生的卵在人类世界孕育，孵化后化身为水精灵返回精灵世界。水精灵保证人类世界拥有充足的降水，使得动植物繁荣地繁衍生息，人类社会可以繁荣安康。水精灵的卵在人类世界孕育时会寄生在一种生物个体体内，这个生物个体被称为“精灵守护者”。当孵化临近时，卵会指引精灵守护者来到人类世界与精灵世界的衔接处，此时与水精灵相克的土精灵会来狩猎精灵守护者，只有成功避开土精灵的狩猎才能使水精灵重返精灵世界。

在《精灵守护者》的故事中，二皇子扎克穆被选为精灵守护者，但是由于史书的错误记载，扎克穆被认为被不祥之物附身，遭到皇室的秘密追杀。女保镖巴鲁萨因为机缘巧合成为他的监护人，带着扎克穆在民间谋生躲避皇室的追杀。随着服务于皇室的观星者揭开大旱之兆的原因，逐渐意识到史书的错误，皇室也开始与巴鲁萨一行人开始合作，争取维护扎克穆的生命和人类世界的繁荣。作为根据儿童文学改编的动画，当然是大团圆结局。

B站上的网友戏称这是一部没有反派登场的故事，事实也是如此，故事中的各个人物之间虽然有着各种各样的矛盾，不管是各为其主还是历史偏见，但各个角色都拥有绝对正派的世界观。观众可以从各个角色身上体察到一种正向的人格，比如二皇子扎克穆的勇敢善良，女保镖巴鲁萨对誓言和生命的信仰，观星者修伽对真理的追求，扎克穆母亲二之妃的母爱，巴鲁萨养父吉古洛对知己的忠诚……看完之后会让人有一种充满正能量的感觉。除了这些正能量，动画中的伏笔比比皆是，任何一处细节都会在后续的故事中发挥作用，每每将这些呼应串联起来的时候，都会让人大呼过瘾。

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TITLE: Do semantic parts emerge in Convolutional Neural Networks?

AUTHER: Abel Gonzalez-Garica, David Modolo, Vittorio Ferrari

ASSOCIATION: CLAVIN, University of Edingburgh, UK

FROM: arXiv:1607.03738

CONTRIBUTIONS

  1. An extensive quantitative analysis of the association between responses of CNN filters and sematic parts

METHOD

  1. CNNs are trained for object detection task or object classification.
  2. Filters that give significant responses to certain semantic parts are selected.
  3. Filters are comibned to construct a part detector if necessary.
  4. A regressor is trained for part bounding-boxes.
  5. Discriminative filters are selected in object classification task.

Observation

There are several interesting observatoins from the authers.

Differences between layers. Overall, the higher the network layer, the higher the performance. It means that in higher part of the network abstract semantic contents are represented.

Differences between part classes. Performance varies greatly across part classes. It seems that very discriminative semantic parts are well detected.

Filter combinations. Performing part detection using a combination of filters always performs better than single best filter. It means taht a semantic part may be represented jointly by several filters.

Filter sharing across part classes. Filters are shared across different part classes. It is clear that some filters are representative for a generic part and work well on all object classes containing it.

The number of emerged semantic parts. Only a modest number of filters responses to semantic parts. The auther concludes that the network does contain filters combinations that can cover some part classes well, but they do not fire exclusively on the part, making them weak part detectors. Moreover, the part classes covered by the semantic filters tend to either cover a large image area, or be very discriminative for their object class.

Discriminative filters in object classification. The filters are measured by how much they contribute to the classification score. On average, 9/256 filters are discriminative for a particular class. The total number of dicriminative filte overall 16 object classes amounts to 104. It shows that the discriminative filters are largely distributed across different object classes, with very little sharing.

Discriminative and semantic filters. 5.5 out of the 9 discriminative filters for an object class are semantic filters.It means that only a portion of the filters learned by CNN are semantic, and many are just responding to dicriminative patches.

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在学校读研期间研读过随机森林的相关文献，也实际使用过，算是对随机森林有些了解。今天看到一篇博文，感觉对随机森林的总结比较到位，遂转发到自己的个人网站上。原文链接为[Machine Learning & Algorithm] 随机森林（Random Forest）

什么是随机森林？

作为新兴起的、高度灵活的一种机器学习算法，随机森林（Random Forest，简称RF）拥有广泛的应用前景，从市场营销到医疗保健保险，既可以用来做市场营销模拟的建模，统计客户来源，保留和流失，也可用来预测疾病的风险和病患者的易感性。最初，我是在参加校外竞赛时接触到随机森林算法的。最近几年的国内外大赛，包括2013年百度校园电影推荐系统大赛、2014年阿里巴巴天池大数据竞赛以及Kaggle数据科学竞赛，参赛者对随机森林的使用占有相当高的比例。此外，据我的个人了解来看，一大部分成功进入答辩的队伍也都选择了Random Forest 或者 GBDT 算法。所以可以看出，Random Forest在准确率方面还是相当有优势的。

那说了这么多，那随机森林到底是怎样的一种算法呢？

如果读者接触过决策树（Decision Tree）的话，那么会很容易理解什么是随机森林。随机森林就是通过集成学习的思想将多棵树集成的一种算法，它的基本单元是决策树，而它的本质属于机器学习的一大分支——集成学习（Ensemble Learning）方法。随机森林的名称中有两个关键词，一个是“随机”，一个就是“森林”。“森林”我们很好理解，一棵叫做树，那么成百上千棵就可以叫做森林了，这样的比喻还是很贴切的，其实这也是随机森林的主要思想–集成思想的体现。“随机”的含义我们会在下边部分讲到。

其实从直观角度来解释，每棵决策树都是一个分类器（假设现在针对的是分类问题），那么对于一个输入样本，N棵树会有N个分类结果。而随机森林集成了所有的分类投票结果，将投票次数最多的类别指定为最终的输出，这就是一种最简单的 Bagging 思想。

随机森林的特点

我们前边提到，随机森林是一种很灵活实用的方法，它有如下几个特点：

  1. 在当前所有算法中，具有极好的准确率/It is unexcelled in accuracy among current algorithms；
  2. 能够有效地运行在大数据集上/It runs efficiently on large data bases；
  3. 能够处理具有高维特征的输入样本，而且不需要降维/It can handle thousands of input variables without variable deletion；
  4. 能够评估各个特征在分类问题上的重要性/It gives estimates of what variables are important in the classification；
  5. 在生成过程中，能够获取到内部生成误差的一种无偏估计/It generates an internal unbiased estimate of the generalization error as the forest building progresses；
  6. 对于缺省值问题也能够获得很好得结果/It has an effective method for estimating missing data and maintains accuracy when a large proportion of the data are missing

实际上，随机森林的特点不只有这六点，它就相当于机器学习领域的Leatherman（多面手），你几乎可以把任何东西扔进去，它基本上都是可供使用的。在估计推断映射方面特别好用，以致都不需要像SVM那样做很多参数的调试。具体的随机森林介绍可以参见随机森林主页：Random Forest。

###随机森林的相关基础知识###

随机森林看起来是很好理解，但是要完全搞明白它的工作原理，需要很多机器学习方面相关的基础知识。在本文中，我们简单谈一下，而不逐一进行赘述，如果有同学不太了解相关的知识，可以参阅其他博友的一些相关博文或者文献。

信息、熵以及信息增益的概念，这三个基本概念是决策树的根本，是决策树利用特征来分类时，确定特征选取顺序的依据。理解了它们，决策树你也就了解了大概。

引用香农的话来说，信息是用来消除随机不确定性的东西。当然这句话虽然经典，但是还是很难去搞明白这种东西到底是个什么样，可能在不同的地方来说，指的东西又不一样。对于机器学习中的决策树而言，如果带分类的事物集合可以划分为多个类别当中，熵是用来度量不确定性的，当熵越大，X=xi的不确定性越大，反之越小。对于机器学习中的分类问题而言，熵越大即这个类别的不确定性更大，反之越小。

信息增益在决策树算法中是用来选择特征的指标，信息增益越大，则这个特征的选择性越好。

决策树是一种树形结构，其中每个内部节点表示一个属性上的测试，每个分支代表一个测试输出，每个叶节点代表一种类别。常见的决策树算法有C4.5、ID3和CART。

集成学习通过建立几个模型组合的来解决单一预测问题。它的工作原理是生成多个分类器/模型，各自独立地学习和作出预测。这些预测最后结合成单预测，因此优于任何一个单分类的做出预测。

随机森林是集成学习的一个子类，它依靠于决策树的投票选择来决定最后的分类结果。你可以在这找到用python实现集成学习的文档：Scikit 学习文档。

随机森林的生成

前面提到，随机森林中有许多的分类树。我们要将一个输入样本进行分类，我们需要将输入样本输入到每棵树中进行分类。打个形象的比喻：森林中召开会议，讨论某个动物到底是老鼠还是松鼠，每棵树都要独立地发表自己对这个问题的看法，也就是每棵树都要投票。该动物到底是老鼠还是松鼠，要依据投票情况来确定，获得票数最多的类别就是森林的分类结果。森林中的每棵树都是独立的，99.9%不相关的树做出的预测结果涵盖所有的情况，这些预测结果将会彼此抵消。少数优秀的树的预测结果将会超脱于芸芸“噪音”，做出一个好的预测。将若干个弱分类器的分类结果进行投票选择，从而组成一个强分类器，这就是随机森林bagging的思想（关于bagging的一个有必要提及的问题：bagging的代价是不用单棵决策树来做预测，具体哪个变量起到重要作用变得未知，所以bagging改进了预测准确率但损失了解释性）。

有了树我们就可以分类了，但是森林中的每棵树是怎么生成的呢？

每棵树的按照如下规则生成：

  1. 如果训练集大小为N，对于每棵树而言，随机且有放回地从训练集中的抽取N个训练样本（这种采样方式称为bootstrap sample方法），作为该树的训练集；

  2. 从这里我们可以知道：每棵树的训练集都是不同的，而且里面包含重复的训练样本（理解这点很重要）。

    为什么要随机抽样训练集？

    如果不进行随机抽样，每棵树的训练集都一样，那么最终训练出的树分类结果也是完全一样的，这样的话完全没有bagging的必要；

    为什么要有放回地抽样？

    我理解的是这样的：如果不是有放回的抽样，那么每棵树的训练样本都是不同的，都是没有交集的，这样每棵树都是”有偏的”，都是绝对”片面的”（当然这样说可能不对），也就是说每棵树训练出来都是有很大的差异的；而随机森林最后分类取决于多棵树（弱分类器）的投票表决，这种表决应该是”求同”，因此使用完全不同的训练集来训练每棵树这样对最终分类结果是没有帮助的，这样无异于是”盲人摸象”。

  3. 如果每个样本的特征维度为M，指定一个常数m<<M，随机地从M个特征中选取m个特征子集，每次树进行分裂时，从这m个特征中选择最优的；

  4. 每棵树都尽最大程度的生长，并且没有剪枝过程。

一开始我们提到的随机森林中的“随机”就是指的这里的两个随机性。两个随机性的引入对随机森林的分类性能至关重要。由于它们的引入，使得随机森林不容易陷入过拟合，并且具有很好得抗噪能力（比如：对缺省值不敏感）。

随机森林分类效果（错误率）与两个因素有关：

  1. 森林中任意两棵树的相关性：相关性越大，错误率越大；
  2. 森林中每棵树的分类能力：每棵树的分类能力越强，整个森林的错误率越低。

减小特征选择个数m，树的相关性和分类能力也会相应的降低；增大m，两者也会随之增大。所以关键问题是如何选择最优的m（或者是范围），这也是随机森林唯一的一个参数。
回到顶部

袋外错误率（oob error）###

上面我们提到，构建随机森林的关键问题就是如何选择最优的m，要解决这个问题主要依据计算袋外错误率oob error（out-of-bag error）。

随机森林有一个重要的优点就是，没有必要对它进行交叉验证或者用一个独立的测试集来获得误差的一个无偏估计。它可以在内部进行评估，也就是说在生成的过程中就可以对误差建立一个无偏估计。

我们知道，在构建每棵树时，我们对训练集使用了不同的bootstrap sample（随机且有放回地抽取）。所以对于每棵树而言（假设对于第k棵树），大约有1/3的训练实例没有参与第k棵树的生成，它们称为第k棵树的oob样本。

而这样的采样特点就允许我们进行oob估计，它的计算方式如下（note：以样本为单位）：

  1. 对每个样本，计算它作为oob样本的树对它的分类情况（约1/3的树）；
  2. 然后以简单多数投票作为该样本的分类结果；
  3. 最后用误分个数占样本总数的比率作为随机森林的oob误分率。

oob误分率是随机森林泛化误差的一个无偏估计，它的结果近似于需要大量计算的k折交叉验证。

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本文的内容主要转载自微信公众号“深度学习大讲堂”中的《深度学习中的激活函数导引》一文，可在公众号中阅读全文。

激活函数的定义与作用

在人工神经网络中，神经元节点的激活函数定义了对神经元输出的映射，简单来说，神经元的输出（例如，全连接网络中就是输入向量与权重向量的内积再加上偏置项）经过激活函数处理后再作为输出。激活函数可以定义为一种映射 $h:R\to R$，且几乎处处可导。

神经网络中激活函数的主要作用是提供网络的非线性建模能力，如不特别说明，激活函数一般而言是非线性函数。假设一个示例神经网络中仅包含线性卷积和全连接运算，那么该网络仅能够表达线性映射，即便增加网络的深度也依旧还是线性映射，难以有效建模实际环境中非线性分布的数据。加入（非线性）激活函数之后，深度神经网络才具备了分层的非线性映射学习能力。因此，激活函数是深度神经网络中不可或缺的部分。

几种常用的激活函数

以下摘抄一些常用的激活函数。

  1. Sigmoid是使用范围最广的一类激活函数，具有指数函数形状。Sigmoid函数具有软饱和性，使得深度神经网络在二三十年里一直难以有效的训练，是阻碍神经网络发展的重要原因。具体来说，由于在后向传递过程中，sigmoid向下传导的梯度包含了一个$f’(x)$因子（sigmoid关于输入的导数），因此一旦输入落入饱和区，$f’(x)$就会趋近于0，导致向底层传递的梯度也变得非常小。此时，网络参数很难得到有效训练。这种现象被称为梯度消失。一般来说，使用sigmoid作为激活函数的网络在5层之内就会产生梯度消失现象。梯度消失问题至今仍然存在，但被新的优化方法有效缓解了，例如DBN中的分层预训练，Batch Normalization的逐层归一化等。

    Sigmoid的饱和性虽然会导致梯度消失，但也有其有利的一面。例如它在物理意义上最为接近生物神经元；$(0, 1)$的输出还可以被理解为概率，或用于输入的归一化，例如Sigmoid交叉熵损失函数。

  2. tanh也具有软饱和性。但是使用tanh作为激活函数的网络收敛速度要比sigmoid快。因为tanh的输出均值比sigmoid更接近0，SGD会更接近 natural gradient，从而降低所需的迭代次数。

  3. ReLU与传统的sigmoid激活函数相比，ReLU能够有效缓解梯度消失问题，从而直接以监督的方式训练深度神经网络，无需依赖无监督的逐层预训练，这也是2012年深度卷积神经网络在ILSVRC竞赛中取得里程碑式突破的重要原因之一。

    ReLU在x<0时硬饱和。由于x>0时导数为1，所以，ReLU能够在x>0时保持梯度不衰减，从而缓解梯度消失问题。但随着训练的推进，部分输入会落入硬饱和区，导致对应权重无法更新。这种现象被称为“神经元死亡”。ReLU还经常被“诟病”的一个问题是输出具有偏移现象，即输出均值恒大于零。偏移现象和神经元死亡会共同影响网络的收敛性。

  4. PReLU是ReLU的改进版本，具有非饱和性。与LReLU相比，PReLU中的负半轴斜率a可学习而非固定。虽然PReLU引入了额外的参数，但基本不需要担心过拟合。与ReLU相比，PReLU收敛速度更快。因为PReLU的输出更接近0均值，使得SGD更接近natural gradient。

    原文中有一个论述很有意思。ResNet采用ReLU而非PReLU的原因可能在于：首先，对PReLU采用正则将激活值推向0也能够带来性能提升。这或许表明，小尺度或稀疏激活值对深度网络的影响更大;其次，ResNet中包含单位变换和残差两个分支。残差分支用于学习对单位变换的扰动。如果单位变换是最优解，那么残差分支的扰动应该越小越好。这种假设下，小尺度或稀疏激活值对深度网络的影响更大。此时，ReLU或许是比PReLU更好的选择。

其他激活函数还包括RReLU、Maxout、ELU、Noisy Activation Functions、CReLU、MPELU等，但个人感觉并没有以上几种常用，而且相对来说ReLU和PReLU的应用更广泛。

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