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

  •        42

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.

https://github.com/carpedm20/DiscoGAN-pytorch

Tags
Implementation
License
Platform

   




Related Projects

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

  •    Python

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.

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

  •    Python

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.

PyTorch-GAN - PyTorch implementations of Generative Adversarial Networks.

  •    Python

Collection of PyTorch implementations of Generative Adversarial Network varieties presented in research papers. Model architectures will not always mirror the ones proposed in the papers, but I have chosen to focus on getting the core ideas covered instead of getting every layer configuration right. Contributions and suggestions of GANs to implement are very welcomed. Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128x128 resolution image samples exhibiting global coherence. We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. These analyses demonstrate that high resolution samples provide class information not present in low resolution samples. Across 1000 ImageNet classes, 128x128 samples are more than twice as discriminable as artificially resized 32x32 samples. In addition, 84.7% of the classes have samples exhibiting diversity comparable to real ImageNet data.

PyTorch-Tutorial - Build your neural network easy and fast

  •    Jupyter

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.

T2F - T2F: text to face generation using Deep Learning

  •    Python

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.


iGAN - Interactive Image Generation via Generative Adversarial Networks

  •    Python

[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.

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

  •    Python

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.

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

  •    Python

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.

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

  •    Python

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.

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

  •    Lua

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.

BicycleGAN - [NIPS 2017] Toward Multimodal Image-to-Image Translation

  •    Python

Pytorch implementation for multimodal image-to-image translation. For example, given the same night image, our model is able to synthesize possible day images with different types of lighting, sky and clouds. The training requires paired data. Note: The current software works well with PyTorch 0.4. Check out the older branch that supports PyTorch 0.1-0.3.

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

  •    Python

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.

pytorch-openai-transformer-lm - A PyTorch implementation of OpenAI's finetuned transformer language model with a script to import the weights pre-trained by OpenAI

  •    Python

This is a PyTorch implementation of the TensorFlow code provided with OpenAI's paper "Improving Language Understanding by Generative Pre-Training" by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever. This implementation comprises a script to load in the PyTorch model the weights pre-trained by the authors with the TensorFlow implementation.

pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs

  •    Python

Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic image-to-image translation. It can be used for turning semantic label maps into photo-realistic images or synthesizing portraits from face label maps. High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs Ting-Chun Wang1, Ming-Yu Liu1, Jun-Yan Zhu2, Andrew Tao1, Jan Kautz1, Bryan Catanzaro1 1NVIDIA Corporation, 2UC Berkeley In arxiv, 2017.

context-encoder - [CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs

  •    Lua

If you could successfully run the above demo, run following steps to train your own context encoder model for image inpainting. Features for context encoder trained with reconstruction loss.

torch-light - Deep-learning by using Pytorch

  •    Python

This repository includes basics and advanced examples for deep learning by using Pytorch. Basics which are basic nns like Logistic, CNN, RNN, LSTM are implemented with few lines of code, advanced examples are implemented by complex model. It is better finish Official Pytorch Tutorial before this.

pytorch-summary - Model summary in PyTorch similar to `model.summary()` in Keras

  •    Python

Keras has a neat API to view the visualization of the model which is very helpful while debugging your network. Here is a barebone code to try and mimic the same in PyTorch. The aim is to provide information complementary to, what is not provided by print(your_model) in PyTorch.