原始链接
本文收录了大量的pytorch实现的源码。有入门级的例子说明，也有场景应用实例，更有论文源码的实现。总之，先给记下来。
本文涵盖以下部分：
-入门系列教程
-入门实例
-图像，视觉，CNN相关实现
-GAN相关实现
-NLP相关实现
-先进视觉推理系统
-深度强化学习相关实现
-通用神经网络高级应用
入门系列教程
- pytorch tutorial
https://github.com/MorvanZhou/PyTorch-Tutorial.git - Deep Learning with PyTorch: a 60-minute blitz （来自官网）
http://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html - Simple examples to introduce PyTorch
(通过实例的方式，讲解pytorch的基本原理）
https://github.com/jcjohnson/pytorch-examples.git
入门实例
- Ten minutes pyTorch Tutorial
https://github.com/SherlockLiao/pytorch-beginner.git Offical PyTorch Example
https://github.com/pytorch/examples
包括
Minst Convenets,
Word level Language Modeling using LSTM RNNs,
Training Imagenet Classifiers with Residual Networks,
Generative Adversarial Networks (DCGAN),
Superresolution using an efficient sub-pixel convolutional neural network,
Hogwild training of shared ConvNets across multiple processes on MNIST
Training a CartPole to balance in OpenAI Gym with actor-critic
Natural Language Inference (SNLI) with GloVe vectors, LSTMs, and torchtext
Time sequence prediction - create an LSTM to learn Sine wavesPyTorch Tutorial for Deep Learning Researchers
https://github.com/yunjey/pytorch-tutorial.git
更适合深度学习科研人员。每个实例的代码控制在30行左右，简单易懂。包括
PyTorch Basics
Linear Regression
Logistic Regression
Feedforward Neural Network
Convolutional Neural Network
Deep Residual Network
Recurrent Neural Network
Bidirectional Recurrent Neural Network
Language Model (RNN-LM)
Generative Adversarial Network
Image Captioning (CNN-RNN)
Deep Convolutional GAN (DCGAN)
Variational Auto-Encoder
Neural Style Transfer
TensorBoard in PyTorchPyTorch-playground
https://github.com/aaron-xichen/pytorch-playground.git
初学者游乐场，针对以下常用的数据集，已经写好了一些模型，所以可以玩玩。
目前支持的数据集包括：
mnist, svhn
cifar10, cifar100
stl10
支持的模型包括：
alexnet
vgg16, vgg16_bn, vgg19, vgg19_bn
resnet18, resnet34, resnet50, resnet101, resnet152
squeezenet_v0, squeezenet_v1
inception_v3
图像，视觉，CNN相关实现
- PyTorch-FCN
https://github.com/wkentaro/pytorch-fcn.git
FCN(Fully Convolutional Networks implemented) 的PyTorch实现。 - Attention Transfer
https://github.com/szagoruyko/attention-transfer.git
论文 “Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer” 的PyTorch实现。 - Wide ResNet model in PyTorch
https://github.com/szagoruyko/functional-zoo.git
一个PyTorch实现的 ImageNet Classification 。 - CRNN for image-based sequence recognition
https://github.com/bgshih/crnn.git
这个是 Convolutional Recurrent Neural Network (CRNN) 的 PyTorch 实现。CRNN 由一些CNN，RNN和CTC组成，常用于基于图像的序列识别任务，例如场景文本识别和OCR。 - Scaling the Scattering Transform: Deep Hybrid Networks
https://github.com/edouardoyallon/pyscatwave.git
使用了“scattering network”的CNN实现，特别的构架提升了网络的效果。 - Conditional Similarity Networks (CSNs)
https://github.com/andreasveit/conditional-similarity-networks.git
《Conditional Similarity Networks》的PyTorch实现。 - Multi-style Generative Network for Real-time Transfer
https://github.com/zhanghang1989/PyTorch-Style-Transfer.git
MSG-Net 以及 Neural Style 的 PyTorch 实现。 - Big batch training
https://github.com/eladhoffer/bigBatch.git
《Train longer, generalize better: closing the generalization gap in large batch training of neural networks》的 PyTorch 实现。 - CortexNet
https://github.com/e-lab/pytorch-CortexNet.git
一个使用视频训练的鲁棒预测深度神经网络。 - Neural Message Passing for Quantum Chemistry
https://github.com/priba/nmp_qc.git
论文《Neural Message Passing for Quantum Chemistry》的PyTorch实现，好像是讲计算机视觉下的神经信息传递。
GAN相关实现
- Generative Adversarial Networks (GANs) in PyTorch
https://github.com/devnag/pytorch-generative-adversarial-networks.git
一个非常简单的由PyTorch实现的对抗生成网络 - DCGAN & WGAN with Pytorch
https://github.com/chenyuntc/pytorch-GAN.git
由中国网友实现的DCGAN和WGAN，代码很简洁。 - Official Code for WGAN
https://github.com/martinarjovsky/WassersteinGAN.git
WGAN的官方PyTorch实现。 - DiscoGAN in PyTorch
https://github.com/carpedm20/DiscoGAN-pytorch.git
《Learning to Discover Cross-Domain Relations with Generative Adversarial Networks》的 PyTorch 实现。 - Adversarial Generator-Encoder Network
https://github.com/DmitryUlyanov/AGE.git
《Adversarial Generator-Encoder Networks》的 PyTorch 实现。 - CycleGAN and pix2pix in PyTorch
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix.git
图到图的翻译，著名的 CycleGAN 以及 pix2pix 的PyTorch 实现。 - Weight Normalized GAN
https://github.com/stormraiser/GAN-weight-norm.git
《On the Effects of Batch and Weight Normalization in Generative Adversarial Networks》的 PyTorch 实现。
NLP相关实现
- DeepLearningForNLPInPytorch
https://github.com/rguthrie3/DeepLearningForNLPInPytorch.git
一套以 NLP 为主题的 PyTorch 基础教程。本教程使用Ipython Notebook编写，看起来很直观，方便学习。 - Practial Pytorch with Topic RNN & NLP
https://github.com/spro/practical-pytorch
以 RNN for NLP 为出发点的 PyTorch 基础教程，分为“RNNs for NLP”和“RNNs for timeseries data”两个部分。 - PyOpenNMT: Open-Source Neural Machine Translation
https://github.com/OpenNMT/OpenNMT-py.git
一套由PyTorch实现的机器翻译系统。 - Deal or No Deal? End-to-End Learning for Negotiation Dialogues
https://github.com/facebookresearch/end-to-end-negotiator.git
Facebook AI Research 论文《Deal or No Deal? End-to-End Learning for Negotiation Dialogues》的 PyTorch 实现。 - Attention is all you need: A Pytorch Implementation
https://github.com/jadore801120/attention-is-all-you-need-pytorch.git
Google Research 著名论文《Attention is all you need》的PyTorch实现。 - Improved Visual Semantic Embeddings
https://github.com/fartashf/vsepp.git
一种从图像中检索文字的方法，来自论文：《VSE++: Improved Visual-Semantic Embeddings》。 - Reading Wikipedia to Answer Open-Domain Questions
https://github.com/facebookresearch/DrQA.git
一个开放领域问答系统DrQA的PyTorch实现。 - Structured-Self-Attentive-Sentence-Embedding
https://github.com/ExplorerFreda/Structured-Self-Attentive-Sentence-Embedding.git
IBM 与 MILA 发表的《A Structured Self-Attentive Sentence Embedding》的开源实现。
先进视觉推理系统
- Visual Question Answering in Pytorch
https://github.com/Cadene/vqa.pytorch.git
一个PyTorch实现的优秀视觉推理问答系统，是基于论文《MUTAN: Multimodal Tucker Fusion for Visual Question Answering》实现的。项目中有详细的配置使用方法说明。 - levr-IEP
https://github.com/facebookresearch/clevr-iep.git
Facebook Research 论文《Inferring and Executing Programs for Visual Reasoning》的PyTorch实现，讲的是一个可以基于图片进行关系推理问答的网络。
深度强化学习相关实现
- Deep Reinforcement Learning withpytorch & visdom
https://github.com/onlytailei/pytorch-rl.git
多种使用PyTorch实现强化学习的方法。 - Value Iteration Networks in PyTorch
https://github.com/onlytailei/Value-Iteration-Networks-PyTorch.git
Value Iteration Networks (VIN) 的PyTorch实现。 - A3C in PyTorch
https://github.com/onlytailei/A3C-PyTorch.git
Adavantage async Actor-Critic (A3C) 的PyTorch实现。
通用神经网络高级应用
- PyTorch-meta-optimizer
https://github.com/ikostrikov/pytorch-meta-optimizer.git
论文《Learning to learn by gradient descent by gradient descent》的PyTorch实现。 - OptNet: Differentiable Optimization as a Layer in Neural Networks
https://github.com/locuslab/optnet.git
论文《Differentiable Optimization as a Layer in Neural Networks》的PyTorch实现。 - Task-based End-to-end Model Learning
https://github.com/locuslab/e2e-model-learning.git
论文《Task-based End-to-end Model Learning》的PyTorch实现。 - DiracNets
https://github.com/szagoruyko/diracnets.git
不使用“Skip-Connections”而搭建特别深的神经网络的方法。 - ODIN: Out-of-Distribution Detector for Neural Networks
https://github.com/ShiyuLiang/odin-pytorch.git
这是一个能够检测“分布不足”（Out-of-Distribution)样本的方法的PyTorch实现。当“true positive rate”为95％时，该方法将DenseNet（适用于CIFAR-10）的“false positive rate”从34.7％降至4.3％。 - Accelerate Neural Net Training by Progressively Freezing Layers
https://github.com/ajbrock/FreezeOut.git
一种使用“progressively freezing layers”来加速神经网络训练的方法。 - Efficient_densenet_pytorch
https://github.com/gpleiss/efficient_densenet_pytorch.git
DenseNets的PyTorch实现，优化以节省GPU内存。