Self-Attention-GAN - Pytorch implementation of Self-Attention Generative Adversarial Networks (SAGAN)

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Han Zhang, Ian Goodfellow, Dimitris Metaxas and Augustus Odena, "Self-Attention Generative Adversarial Networks." arXiv preprint arXiv:1805.08318 (2018). This repository provides a PyTorch implementation of SAGAN. Both wgan-gp and wgan-hinge loss are ready, but note that wgan-gp is somehow not compatible with the spectral normalization. Remove all the spectral normalization at the model for the adoption of wgan-gp.

wgan-gp - A pytorch implementation of Paper "Improved Training of Wasserstein GANs"

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An pytorch implementation of Paper "Improved Training of Wasserstein GANs". Some Sample Result, you can refer to the results/toy/ folder for details.

wgan-gp pytorch

iGAN - Interactive Image Generation via Generative Adversarial Networks

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[Project] [Youtube] [Paper] A research prototype developed by UC Berkeley and Adobe CTL. Latest development: [pix2pix]: Torch implementation for learning a mapping from input images to output images. [CycleGAN]: Torch implementation for learning an image-to-image translation (i.e. pix2pix) without input-output pairs. [pytorch-CycleGAN-and-pix2pix]: PyTorch implementation for both unpaired and paired image-to-image translation.

generative-adversarial-network image-manipulation computer-graphics computer-vision gan pix2pix dcgan deep-learning

GAN - Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN

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All have been tested with python2.7+ and tensorflow1.0+ in linux. The final layer can be sigmoid(data: [0,1]) or tanh(data:[-1,1]), my codes all use sigmoid. Using weights_initializer=tf.random_normal_initializer(0, 0.02) will converge faster.

gan dcgan wasserstein-gan infogan adversarial-nets

CycleGAN - Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more

•    Lua

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This package includes CycleGAN, pix2pix, as well as other methods like BiGAN/ALI and Apple's paper S+U learning. The code was written by Jun-Yan Zhu and Taesung Park. Note: Please check out PyTorch implementation for CycleGAN and pix2pix. The PyTorch version is under active development and can produce results comparable or better than this Torch version.

gan generative-adversarial-network deep-learning image-generation image-manipulation cyclegan pix2pix gans computer-vision computer-graphics torch

pytorch-CycleGAN-and-pix2pix - Image-to-image translation in PyTorch (e

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This is our PyTorch implementation for both unpaired and paired image-to-image translation. It is still under active development. The code was written by Jun-Yan Zhu and Taesung Park, and supported by Tongzhou Wang.

pytorch gan cyclegan pix2pix deep-learning computer-vision computer-graphics image-manipulation image-generation generative-adversarial-network gans

chainer-gan-lib - Chainer implementation of recent GAN variants

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This repository collects chainer implementation of state-of-the-art GAN algorithms. These codes are evaluated with the inception score on Cifar-10 dataset. Note that our codes are not faithful re-implementation of the original paper. This implementation has been tested with the following versions.

deep-learning generative-adversarial-network dcgan wgan-gp

dlwin - GPU-accelerated Deep Learning on Windows 10 native

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There are certainly a lot of guides to assist you build great deep learning (DL) setups on Linux or Mac OS (including with Tensorflow which, unfortunately, as of this posting, cannot be easily installed on Windows), but few care about building an efficient Windows 10-native setup. Most focus on running an Ubuntu VM hosted on Windows or using Docker, unnecessary - and ultimately sub-optimal - steps. We also found enough misguiding/deprecated information out there to make it worthwhile putting together a step-by-step guide for the latest stable versions of Keras, Tensorflow, CNTK, MXNet, and PyTorch. Used either together (e.g., Keras with Tensorflow backend), or independently -- PyTorch cannot be used as a Keras backend, TensorFlow can be used on its own -- they make for some of the most powerful deep learning python libraries to work natively on Windows.

theano gpu-acceleration deep-learning tensorflow cudnn cntk gpu-mode keras

Practical_RL - A course in reinforcement learning in the wild

•    Jupyter

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A course on reinforcement learning in the wild. Taught on-campus at HSE and YSDA and maintained to be friendly to online students (both english and russian). The syllabus is approximate: the lectures may occur in a slightly different order and some topics may end up taking two weeks.

reinforcement-learning course-materials deep-learning deep-reinforcement-learning git-course mooc theano lasagne tensorflow pytorch pytorch-tutorials keras

alpha-zero-general - A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4

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A simplified, highly flexible, commented and (hopefully) easy to understand implementation of self-play based reinforcement learning based on the AlphaGo Zero paper (Silver et al). It is designed to be easy to adopt for any two-player turn-based adversarial game and any deep learning framework of your choice. A sample implementation has been provided for the game of Othello in PyTorch, Keras and TensorFlow. An accompanying tutorial can be found here. We also have implementations for GoBang and TicTacToe. To use a game of your choice, subclass the classes in Game.py and NeuralNet.py and implement their functions. Example implementations for Othello can be found in othello/OthelloGame.py and othello/{pytorch,keras,tensorflow}/NNet.py.

tensorflow pytorch keras gobang gomoku alpha-zero alphago-zero alphago reinforcement-learning self-play mcts monte-carlo-tree-search othello tf deep-learning alphazero

pix2pix-tensorflow - TensorFlow implementation of "Image-to-Image Translation Using Conditional Adversarial Networks"

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TensorFlow implementation of Image-to-Image Translation Using Conditional Adversarial Networks that learns a mapping from input images to output images. Note: To avoid the fast convergence of D (discriminator) network, G (generator) network is updated twice for each D network update, which differs from original paper but same as DCGAN-tensorflow, which this project based on.

deepo - A series of Docker images (and their generator) that allows you to quickly set up your deep learning research environment

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If you want to share your data and configurations between the host (your machine or VM) and the container in which you are using Deepo, use the -v option, e.g. This will make /host/data from the host visible as /data in the container, and /host/config as /config. Such isolation reduces the chances of your containerized experiments overwriting or using wrong data.

deep-learning jupyter lasagne caffe tensorflow sonnet keras theano chainer torch pytorch mxnet cntk dockerfile-generator docker-image caffe2 onnx

pycadl - Python package with source code from the course "Creative Applications of Deep Learning w/ TensorFlow"

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This package is part of the Kadenze Academy program Creative Applications of Deep Learning w/ TensorFlow. from cadl import and then pressing tab to see the list of available modules.

deep-learning neural-network tutorial mooc gan vae vae-gan pixelcnn wavenet magenta nsynth tensorflow celeba cyclegan dcgan word2vec glove autoregressive conditional course

tensorlayer-tricks - How to use TensorLayer

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While research in Deep Learning continues to improve the world, we use a bunch of tricks to implement algorithms with TensorLayer day to day. Here are a summary of the tricks to use TensorLayer. If you find a trick that is particularly useful in practice, please open a Pull Request to add it to the document. If we find it to be reasonable and verified, we will merge it in.

tensorlayer tensorflow deep-learning machine-learning data-science neural-network reinforcement-learning neural-networks tensorflow-tutorials tensorflow-models computer-vision tensorflow-framework tensorflow-library tflearn keras tensorboard nlp natural-language-processing lasagne tensorflow-experiments

neuropod - A uniform interface to run deep learning models from multiple frameworks

•    C++

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Neuropod is a library that provides a uniform interface to run deep learning models from multiple frameworks in C++ and Python. Neuropod makes it easy for researchers to build models in a framework of their choosing while also simplifying productionization of these models. It currently supports TensorFlow, PyTorch, TorchScript, Keras and Ludwig.

machine-learning deep-learning tensorflow keras inference pytorch machinelearning deeplearning incubation

InfoGAN - Code for reproducing key results in the paper "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets"

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Code for reproducing key results in the paper InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets by Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel. This project currently requires the dev version of TensorFlow available on Github: https://github.com/tensorflow/tensorflow. As of the release, the latest commit is 79174a.

XLearning - AI on Hadoop

•    Java

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XLearning is a convenient and efficient scheduling platform combined with the big data and artificial intelligence, support for a variety of machine learning, deep learning frameworks. XLearning is running on the Hadoop Yarn and has integrated deep learning frameworks such as TensorFlow, MXNet, Caffe, Theano, PyTorch, Keras, XGBoost. XLearning has the satisfactory scalability and compatibility.Besides the distributed mode of TensorFlow and MXNet frameworks, XLearning supports the standalone mode of all deep learning frameworks such as Caffe, Theano, PyTorch. Moreover, XLearning allows the custom versions and multi-version of frameworks flexibly.

hadoop tensorflow caffe mxnet yarn

ml-workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.

•    Jupyter

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The ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. This workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch, Keras, Sklearn) and dev tools (e.g., Jupyter, VS Code, Tensorboard) perfectly configured, optimized, and integrated. The workspace requires Docker to be installed on your machine (📖 Installation Guide).

nlp docker kubernetes data-science machine-learning r deep-learning jupyter anaconda tensorflow gpu scikit-learn vscode jupyter-notebook data-visualization pytorch neural-networks data-analysis jupyter-lab

crfasrnn_keras - CRF-RNN Keras/Tensorflow version

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This repository contains Keras/Tensorflow code for the "CRF-RNN" semantic image segmentation method, published in the ICCV 2015 paper Conditional Random Fields as Recurrent Neural Networks. This paper was initially described in an arXiv tech report. The online demo of this project won the Best Demo Prize at ICCV 2015. Original Caffe-based code of this project can be found here. Results produced with this Keras/Tensorflow code are almost identical to that with the Caffe-based version. The root directory of the clone will be referred to as crfasrnn_keras hereafter.

image-segmentation semantic-segmentation crf-as-rnn tensorflow keras crfasrnn crfasrnn-keras crfasrnn-tensorflow crf-rnn-tensorflow crf-rnn-keras

face2face-demo - pix2pix demo that learns from facial landmarks and translates this into a face

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This is a pix2pix demo that learns from facial landmarks and translates this into a face. A webcam-enabled application is also provided that translates your face to the trained face in real-time. If you want to download my dataset, here is also the video file that I used and the generated training dataset (400 images already split into training and validation).

tensorflow python3 pix2pix-tensorflow