SUMMARY.md

Table of contents

• 前言
• 第一章：经典网络
  • ImageNet Classification with Deep Convolutional Neural Network
  • Very Deep Convolutional Networks for Large-Scale Image Recognition
  • Going Deeper with Convolutions
  • Deep Residual Learning for Image Recognition
  • PolyNet: A Pursuit of Structural Diversity in Very Deep Networks
  • Squeeze-and-Excitation Networks
  • Densely Connected Convolutional Networks
  • SQUEEZENET: ALEXNET-LEVEL ACCURACY WITH 50X FEWER PARAMETERS AND <0.5MB MODEL SIZE
  • MobileNet v1 and MobileNet v2
  • Xception: Deep Learning with Depthwise Separable Convolutions
  • Aggregated Residual Transformations for Deep Neural Networks
  • ShuffleNet v1 and ShuffleNet v2
  • CondenseNet: An Efficient DenseNet using Learned Group Convolution
  • Neural Architecture Search with Reinforecement Learning
  • Learning Transferable Architectures for Scalable Image Recognition
  • Progressive Neural Architecture Search
  • Regularized Evolution for Image Classifier Architecture Search
  • 实例解析：12306验证码破解
• 第二章：自然语言处理
  • Recurrent Neural Network based Language Model
  • Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
  • Neural Machine Translation by Jointly Learning to Align and Translate
  • Hierarchical Attention Networks for Document Classification
  • Connectionist Temporal Classification : Labelling Unsegmented Sequence Data with Recurrent Neural Ne
  • About Long Short Term Memory
  • Attention Is All you Need
  • BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• 第三章：语音识别
  • Speech Recognition with Deep Recurrent Neural Network
• 第四章：物体检测
  • Rich feature hierarchies for accurate object detection and semantic segmentation
  • Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
  • Fast R-CNN
  • Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
  • R-FCN: Object Detection via Region-based Fully Convolutuional Networks
  • Mask R-CNN
  • You Only Look Once: Unified, Real-Time Object Detection
  • SSD: Single Shot MultiBox Detector
  • YOLO9000: Better, Faster, Stronger
  • Focal Loss for Dense Object Detection
  • YOLOv3: An Incremental Improvement
  • Learning to Segment Every Thing
  • SNIPER: Efficient Multi-Scale Training
• 第五章：光学字符识别
  • 场景文字检测
    • DeepText: A Unified Framework for Text Proposal Generation and Text Detection in Natural Images
    • Detecting Text in Natural Image with Connectionist Text Proposal Network
    • Scene Text Detection via Holistic, Multi-Channel Prediction
    • Arbitrary-Oriented Scene Text Detection via Rotation Proposals
    • PixelLink: Detecting Scene Text via Instance Segmentation
  • 文字识别
    • Spatial Transform Networks
    • Robust Scene Text Recognition with Automatic Rectification
    • Bidirectional Scene Text Recognition with a Single Decoder
    • multi-task learning for text recognition with joint CTC-attention
  • 端到端文字检测与识别
    • Reading Text in the Wild with Convolutional Neural Networks
    • Deep TextSpotter: An End-to-End Trainable Scene Text Localization and Recognition Framework
  • 实例解析：字符验证码破解
  • 二维信息识别
    • 基于Seq2Seq的公式识别引擎
    • Show and Tell: A Neural Image Caption Generator
    • Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• 第六章：语义分割
  • U-Net: Convolutional Networks for Biomedical Image Segmentation
• 第七章：人脸识别
  • 人脸检测
    • DenseBox: Unifying Landmark Localization with End to End Object Detection
    • UnitBox: An Advanced Object Detection Network
• 第八章：网络优化
  • Batch Normalization
  • Layer Normalization
  • Weight Normalization
  • Instance Normalization
  • Group Normalization
  • Switchable Normalization
• 第九章：生成对抗网络
  • Generative Adversarial Nets
• 其它应用
  • Holistically-Nested Edge Detection
  • Image Style Transfer Using Convolutional Nerual Networks
  • Background Matting： The World is Your Green Screen
• Tags
• References