原始鏈接
本文收錄了大量的pytorch實現的源碼。有入門級的例子說明，也有場景應用實例，更有論文源碼的實現。總之，先給記下來。
本文涵蓋以下部分：
-入門系列教程
-入門實例
-圖像，視覺，CNN相關實現
-GAN相關實現
-NLP相關實現
-先進視覺推理系統
-深度強化學習相關實現
-通用神經網絡高級應用
入門系列教程
- pytorch tutorial
https://github.com/MorvanZhou/PyTorch-Tutorial.git - Deep Learning with PyTorch: a 60-minute blitz （來自官網）
http://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html - Simple examples to introduce PyTorch
(通過實例的方式，講解pytorch的基本原理）
https://github.com/jcjohnson/pytorch-examples.git
入門實例
- 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
更適合深度學習科研人員。每個實例的代碼控制在30行左右，簡單易懂。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
初學者遊樂場，針對以下常用的數據集，已經寫好了一些模型，所以可以玩玩。
目前支持的數據集包括：
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
圖像，視覺，CNN相關實現
- PyTorch-FCN
https://github.com/wkentaro/pytorch-fcn.git
FCN(Fully Convolutional Networks implemented) 的PyTorch實現。 - Attention Transfer
https://github.com/szagoruyko/attention-transfer.git
論文 “Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer” 的PyTorch實現。 - Wide ResNet model in PyTorch
https://github.com/szagoruyko/functional-zoo.git
一個PyTorch實現的 ImageNet Classification 。 - CRNN for image-based sequence recognition
https://github.com/bgshih/crnn.git
這個是 Convolutional Recurrent Neural Network (CRNN) 的 PyTorch 實現。CRNN 由一些CNN，RNN和CTC組成，常用於基於圖像的序列識別任務，例如場景文本識別和OCR。 - Scaling the Scattering Transform: Deep Hybrid Networks
https://github.com/edouardoyallon/pyscatwave.git
使用了“scattering network”的CNN實現，特別的構架提升了網絡的效果。 - Conditional Similarity Networks (CSNs)
https://github.com/andreasveit/conditional-similarity-networks.git
《Conditional Similarity Networks》的PyTorch實現。 - Multi-style Generative Network for Real-time Transfer
https://github.com/zhanghang1989/PyTorch-Style-Transfer.git
MSG-Net 以及 Neural Style 的 PyTorch 實現。 - 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 實現。 - CortexNet
https://github.com/e-lab/pytorch-CortexNet.git
一個使用視頻訓練的魯棒預測深度神經網絡。 - Neural Message Passing for Quantum Chemistry
https://github.com/priba/nmp_qc.git
論文《Neural Message Passing for Quantum Chemistry》的PyTorch實現，好像是講計算機視覺下的神經信息傳遞。
GAN相關實現
- Generative Adversarial Networks (GANs) in PyTorch
https://github.com/devnag/pytorch-generative-adversarial-networks.git
一個非常簡單的由PyTorch實現的對抗生成網絡 - DCGAN & WGAN with Pytorch
https://github.com/chenyuntc/pytorch-GAN.git
由中國網友實現的DCGAN和WGAN，代碼很簡潔。 - Official Code for WGAN
https://github.com/martinarjovsky/WassersteinGAN.git
WGAN的官方PyTorch實現。 - DiscoGAN in PyTorch
https://github.com/carpedm20/DiscoGAN-pytorch.git
《Learning to Discover Cross-Domain Relations with Generative Adversarial Networks》的 PyTorch 實現。 - Adversarial Generator-Encoder Network
https://github.com/DmitryUlyanov/AGE.git
《Adversarial Generator-Encoder Networks》的 PyTorch 實現。 - CycleGAN and pix2pix in PyTorch
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix.git
圖到圖的翻譯，著名的 CycleGAN 以及 pix2pix 的PyTorch 實現。 - 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 實現。
NLP相關實現
- DeepLearningForNLPInPytorch
https://github.com/rguthrie3/DeepLearningForNLPInPytorch.git
一套以 NLP 爲主題的 PyTorch 基礎教程。本教程使用Ipython Notebook編寫，看起來很直觀，方便學習。 - Practial Pytorch with Topic RNN & NLP
https://github.com/spro/practical-pytorch
以 RNN for NLP 爲出發點的 PyTorch 基礎教程，分爲“RNNs for NLP”和“RNNs for timeseries data”兩個部分。 - PyOpenNMT: Open-Source Neural Machine Translation
https://github.com/OpenNMT/OpenNMT-py.git
一套由PyTorch實現的機器翻譯系統。 - Deal or No Deal? End-to-End Learning for Negotiation Dialogues
https://github.com/facebookresearch/end-to-end-negotiator.git
Facebook AI Research 論文《Deal or No Deal? End-to-End Learning for Negotiation Dialogues》的 PyTorch 實現。 - Attention is all you need: A Pytorch Implementation
https://github.com/jadore801120/attention-is-all-you-need-pytorch.git
Google Research 著名論文《Attention is all you need》的PyTorch實現。 - Improved Visual Semantic Embeddings
https://github.com/fartashf/vsepp.git
一種從圖像中檢索文字的方法，來自論文：《VSE++: Improved Visual-Semantic Embeddings》。 - Reading Wikipedia to Answer Open-Domain Questions
https://github.com/facebookresearch/DrQA.git
一個開放領域問答系統DrQA的PyTorch實現。 - Structured-Self-Attentive-Sentence-Embedding
https://github.com/ExplorerFreda/Structured-Self-Attentive-Sentence-Embedding.git
IBM 與 MILA 發表的《A Structured Self-Attentive Sentence Embedding》的開源實現。
先進視覺推理系統
- Visual Question Answering in Pytorch
https://github.com/Cadene/vqa.pytorch.git
一個PyTorch實現的優秀視覺推理問答系統，是基於論文《MUTAN: Multimodal Tucker Fusion for Visual Question Answering》實現的。項目中有詳細的配置使用方法說明。 - levr-IEP
https://github.com/facebookresearch/clevr-iep.git
Facebook Research 論文《Inferring and Executing Programs for Visual Reasoning》的PyTorch實現，講的是一個可以基於圖片進行關係推理問答的網絡。
深度強化學習相關實現
- Deep Reinforcement Learning withpytorch & visdom
https://github.com/onlytailei/pytorch-rl.git
多種使用PyTorch實現強化學習的方法。 - Value Iteration Networks in PyTorch
https://github.com/onlytailei/Value-Iteration-Networks-PyTorch.git
Value Iteration Networks (VIN) 的PyTorch實現。 - A3C in PyTorch
https://github.com/onlytailei/A3C-PyTorch.git
Adavantage async Actor-Critic (A3C) 的PyTorch實現。
通用神經網絡高級應用
- PyTorch-meta-optimizer
https://github.com/ikostrikov/pytorch-meta-optimizer.git
論文《Learning to learn by gradient descent by gradient descent》的PyTorch實現。 - OptNet: Differentiable Optimization as a Layer in Neural Networks
https://github.com/locuslab/optnet.git
論文《Differentiable Optimization as a Layer in Neural Networks》的PyTorch實現。 - Task-based End-to-end Model Learning
https://github.com/locuslab/e2e-model-learning.git
論文《Task-based End-to-end Model Learning》的PyTorch實現。 - DiracNets
https://github.com/szagoruyko/diracnets.git
不使用“Skip-Connections”而搭建特別深的神經網絡的方法。 - ODIN: Out-of-Distribution Detector for Neural Networks
https://github.com/ShiyuLiang/odin-pytorch.git
這是一個能夠檢測“分佈不足”（Out-of-Distribution)樣本的方法的PyTorch實現。當“true positive rate”爲95％時，該方法將DenseNet（適用於CIFAR-10）的“false positive rate”從34.7％降至4.3％。 - Accelerate Neural Net Training by Progressively Freezing Layers
https://github.com/ajbrock/FreezeOut.git
一種使用“progressively freezing layers”來加速神經網絡訓練的方法。 - Efficient_densenet_pytorch
https://github.com/gpleiss/efficient_densenet_pytorch.git
DenseNets的PyTorch實現，優化以節省GPU內存。