深度学习（四）转--入门深度学习的一些开源代码

原文作者：aircraft

原文链接：

没错这篇又是转发的，因为觉得学习深度学习难免要从别人的代码开始，所以就转发了。不过转发的时候没找到原作者是谁，所以原作者看到不要打我-------QAQ

语义分割：
Awesome Semantic Segmentation
https://github.com/mrgloom/awesome-semantic-segmentation

Semantic Segmentation Algorithms Implemented in PyTorch
https://github.com/meetshah1995/pytorch-semseg

Learning Deconvolution Network for Semantic Segmentation
https://github.com/HyeonwooNoh/DeconvNet

Fully Convolutional Instance-aware Semantic Segmentation
https://github.com/daijifeng001/TA-FCN

Fully Convolutional Networks for Semantic Segmentation
https://github.com/shelhamer/fcn.berkeleyvision.org

PixelNet: Representation of the pixels, by the pixels, and for the pixels
https://github.com/aayushbansal/PixelNet
http://www.cs.cmu.edu/~aayushb/pixelNet/

ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://hszhao.github.io/projects/icnet/
https://github.com/hszhao/ICNet

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Semantic Pixel-Wise Labelling
https://arxiv.org/pdf/1511.00561.pdf PAMI-2017
https://github.com/alexgkendall/caffe-segnet

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
https://bitbucket.org/aquariusjay/deeplab-public-ver2/overview

DeconvNet : Learning Deconvolution Network for Semantic Segmentation ICCV2015
https://github.com/HyeonwooNoh/DeconvNet
http://cvlab.postech.ac.kr/research/deconvnet/

Pyramid Scene Parsing Network CVPR2017
https://github.com/hszhao/PSPNet

Fully Convolutional Instance-aware Semantic Segmentation CVPR2017
https://github.com/msracver/FCIS

ParseNet: Looking Wider to See Better
https://github.com/weiliu89/caffe/tree/fcn

深度网络模型：
Deep Residual Learning for Image Recognition
https://github.com/KaimingHe/deep-residual-networks

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks
for Real-Time Object Detection for Autonomous Driving
https://github.com/BichenWuUCB/squeezeDet

Coordinating Filters for Faster Deep Neural Networks
https://arxiv.org/abs/1703.09746
https://github.com/wenwei202/caffe/tree/sfm

Network Dissection:
Quantifying Interpretability of Deep Visual Representations
CVPR2017
https://github.com/CSAILVision/NetDissect

人脸识别
C++ 代码： https://github.com/seetaface/SeetaFaceEngine

A Discriminative Feature Learning Approach for Deep Face Recognition
code: https://github.com/ydwen/caffe-face

目标检测：
PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
https://github.com/sanghoon/pva-faster-rcnn

R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/daijifeng001/r-fcn

A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection CVPR 2017
Caffe code : https://github.com/xiaolonw/adversarial-frcnn

Improving Object Detection With One Line of Code
https://github.com/bharatsingh430/soft-nms

行人检测：

Is Faster R-CNN Doing Well for Pedestrian Detection？ ECCV2016
https://github.com/zhangliliang/RPN_BF/tree/RPN-pedestrian

人体姿态估计：
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields CVPR2017
https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation

Convolutional Pose Machines CVPR2016
https://github.com/shihenw/convolutional-pose-machines-release

深度图片风格迁移
Deep Photo Style Transfer
https://github.com/luanfujun/deep-photo-styletransfer

检测
Accurate Single Stage Detector Using Recurrent Rolling Convolution
https://github.com/xiaohaoChen/rrc_detection

原文作者：aircraft

原文链接：

没错这篇又是转发的，因为觉得还不错就转发了，不过转发的时候找不到原作者是谁 所以这里就没有指明了。

人脸修复
Generative Face Completion
https://github.com/Yijunmaverick/GenerativeFaceCompletion

Failures of Gradient-Based Deep Learning
https://github.com/shakedshammah/failures_of_DL

深度视频去模糊
Deep Video Deblurring
https://github.com/shuochsu/DeepVideoDeblurring

深度去噪
Learning Deep CNN Denoiser Prior for Image Restoration
https://github.com/cszn/ircnn

人脸超分辨
Face Super-Resolution Through Wasserstein GANs
https://github.com/MandyZChen/srez
https://github.com/YuguangTong/improved_wgan_training

视频结构化分析平台
https://github.com/AKSHAYUBHAT/DeepVideoAnalytics