gan-playground - GAN Playground - Experiment with Generative Adversarial Nets in your browser

•    TypeScript

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GAN Playground lets you play around with Generative Adversarial Networks right in your browser. Currently, it contains three built-in datasets: MNIST, Fashion MNIST, and CIFAR-10. GAN Playground provides you the ability to set your models' hyperparameters and build up your discriminator and generator layer-by-layer. You can observe the network learn in real time as the generator produces more and more realistic images, or more likely, gets stuck in failure modes such as mode collapse.

generative-adversarial-network deep-learning machine-learning neural-network gan

fashion-mnist - A MNIST-like fashion product database. Benchmark :point_right:

•    Python

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Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.

mnist deep-learning benchmark machine-learning dataset computer-vision fashion fashion-mnist gan zalando convolutional-neural-networks

generative-models - Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

•    Python

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Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. Also present here are RBM and Helmholtz Machine. Generated samples will be stored in GAN/{gan_model}/out (or VAE/{vae_model}/out, etc) directory during training.

vae gan pytorch tensorflow generative-model machine-learning rbm restricted-boltzmann-machine

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

semi-supervised-pytorch - Implementations of different VAE-based semi-supervised and generative models in PyTorch

•    Python

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A PyTorch-based package containing useful models for modern deep semi-supervised learning and deep generative models. Want to jump right into it? Look into the notebooks. 2018.04.17 - The Gumbel softmax notebook has been added to show how you can use discrete latent variables in VAEs. 2018.02.28 - The β-VAE notebook was added to show how VAEs can learn disentangled representations.

semi-supervised-learning pytorch generative-models

OSVOS-PyTorch - PyTorch implementation of One-Shot Video Object Segmentation (OSVOS)

•    Python

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Check our project page for additional information. OSVOS is a method that tackles the task of semi-supervised video object segmentation. It is based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of foreground segmentation, and finally to learning the appearance of a single annotated object of the test sequence (hence one-shot). Experiments on DAVIS 2016 show that OSVOS is faster than currently available techniques and improves the state of the art by a significant margin (79.8% vs 68.0%).

video-segmentation

DiscoGAN-pytorch - PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

•    Jupyter

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PyTorch implementation of Learning to Discover Cross-Domain Relations with Generative Adversarial Networks. * All samples in README.md are genearted by neural network except the first image for each row. * Network structure is slightly diffferent (here) from the author's code.

gan generative-model unsupervised-learning pytorch

srgan - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

•    Python

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We run this script under TensorFlow 1.4 and the TensorLayer 1.8.0+. 🚀 This repo will be moved to here (please star) for life-cycle management soon. More cool Computer Vision applications such as pose estimation and style transfer can be found in this organization.

tensorlayer tensorflow super-resolution gan cnn srgan vgg16 vgg19 vgg

zhihu - This repo contains the source code in my personal column (https://zhuanlan

•    Jupyter

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This repo contains the source code in my personal column (https://zhuanlan.zhihu.com/zhaoyeyu), implemented using Python 3.6. Including Natural Language Processing and Computer Vision projects, such as text generation, machine translation, deep convolution GAN and other actual combat code.

deep-learning tensorflow-examples convolutional-neural-networks recurrent-neural-networks autoencoder gan style-transfer natural-language-processing machine-translation

StarGAN - Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

•    Python

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PyTorch implementation of StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation. StarGAN can flexibly translate an input image to any desired target domain using only a single generator and a discriminator.

stargan gan image-to-image-translation pytorch generative-adversarial-network image-manipulation computer-vision neural-networks

HyperGAN - A composable Generative Adversarial Network(GAN) with API and command line tool.

•    Python

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A composable GAN API and CLI. Built for developers, researchers, and artists. HyperGAN is currently in open beta.

gan supervised-learning unsupervised-learning learning generative-adversarial-network generative-model artificial-intelligence machine-learning machine-learning-api tensorflow classification generator discriminator

densenet.pytorch - A PyTorch implementation of DenseNet.

•    Python

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This is a PyTorch implementation of the DenseNet-BC architecture as described in the paper Densely Connected Convolutional Networks by G. Huang, Z. Liu, K. Weinberger, and L. van der Maaten. This implementation gets a CIFAR-10+ error rate of 4.77 with a 100-layer DenseNet-BC with a growth rate of 12. Their official implementation and links to many other third-party implementations are available in the liuzhuang13/DenseNet repo on GitHub. As this table from the DenseNet paper shows, it provides competitive state of the art results on CIFAR-10, CIFAR-100, and SVHN.

pytorch deep-learning densenet

All-About-the-GAN - All About the GANs(Generative Adversarial Networks) - Summarized lists for GAN

•    Python

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The purpose of this repository is providing the curated list of the state-of-the-art works on the field of Generative Adversarial Networks since their introduction in 2014. You can also check out the same data in a tabular format with functionality to filter by year or do a quick search by title here.

gan adversarial-networks arxiv neural-network unsupervised-learning adversarial-nets image-synthesis deep-learning generative-adversarial-network medical-imaging tensorflow pytorch paper cgan ct-denoising segmentation medical-image-synthesis reconstruction detection classification

TensorFlow-Tutorials - 텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다

•    Python

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텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다. 텐서플로우 공식 사이트에서 제공하는 안내서의 대부분의 내용을 다루고 있으며, 공식 사이트에서 제공하는 소스 코드보다는 훨씬 간략하게 작성하였으므로 쉽게 개념을 익힐 수 있을 것 입니다. 또한, 모든 주석은 한글로(!) 되어 있습니다.

neural-network tensorflow mnist autoencoder rnn deep-learning tutorial chatbot seq2seq dqn word2vec cnn gan inception

T2F - T2F: text to face generation using Deep Learning

•    Python

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Text-to-Face generation using Deep Learning. This project combines two of the recent architectures StackGAN and ProGAN for synthesizing faces from textual descriptions. The project uses Face2Text dataset which contains 400 facial images and textual captions for each of them. The data can be obtained by contacting either the RIVAL group or the authors of the aforementioned paper. The code is present in the implementation/ subdirectory. The implementation is done using the PyTorch framework. So, for running this code, please install PyTorch version 0.4.0 before continuing.

gan generative-adversarial-network adversarial-machine-learning progressively-growing-gan text-to-image

text-classification-models-tf - Tensorflow implementations of Text Classification Models.

•    Python

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Tensorflow implementation of Text Classification Models. Semi-supervised text classification(Transfer learning) models are implemented at [dongjun-Lee/transfer-learning-text-tf].

tensorflow text-classification

awesome-transfer-learning - Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc

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A list of awesome papers and cool resources on transfer learning, domain adaptation and domain-to-domain translation in general! As you will notice, this list is currently mostly focused on domain adaptation (DA), but don't hesitate to suggest resources in other subfields of transfer learning. I accept pull requests. Papers are ordered by theme and inside each theme by publication date (submission date for arXiv papers). If the network or algorithm is given a name in a paper, this one is written in bold before the paper's name.

transfer-learning domain-adaptation unsupervised-learning paper awesome-list

pytorch-book - PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation

•    Jupyter

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PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation

pytorch pytorch-tutorials pytorch-tutorials-cn deep-learning neural-style charrnn gan caption neuraltalk image-classification visdom tensorboard nn tensor autograd jupyter-notebook

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

•    Python

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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

PyTorch-Tutorial - Build your neural network easy and fast

•    Jupyter

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In these tutorials for pyTorch, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. Thanks for liufuyang's notebook files which is a great contribution to this tutorial.

neural-network pytorch-tutorial batch-normalization cnn rnn autoencoder pytorch regression classification batch tutorial dropout dqn reinforcement-learning gan generative-adversarial-network machine-learning