Displaying 1 to 20 from 21 results

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.

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.

the-gan-zoo - A list of all named GANs!

  •    Python

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. Contributions are welcome. Add links through pull requests in gans.tsv file in the same format or create an issue to lemme know something I missed or to start a discussion.




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.

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

  •    Python

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.

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

  •    TypeScript

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-compression - TensorFlow Implementation of Generative Adversarial Networks for Extreme Learned Image Compression

  •    Python

TensorFlow Implementation for learned compression of images using Generative Adversarial Networks. The method was developed by Agustsson et. al. in Generative Adversarial Networks for Extreme Learned Image Compression. The proposed idea is very interesting and their approach is well-described. Training is conducted with batch size 1 and reconstructed samples / tensorboard summaries will be periodically written every certain number of steps (default is 128). Checkpoints are saved every 10 epochs.


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.

video_prediction - Stochastic Adversarial Video Prediction

  •    Python

TensorFlow implementation for stochastic adversarial video prediction. Given a sequence of initial frames, our model is able to predict future frames of various possible futures. For example, in the next two sequences, we show the ground truth sequence on the left and random predictions of our model on the right. Predicted frames are indicated by the yellow bar at the bottom. For more examples, visit the project page. Stochastic Adversarial Video Prediction, Alex X. Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, Sergey Levine. arXiv preprint arXiv:1804.01523, 2018.

tgan - The implementation of Temporal Generative Adversarial Nets with Singular Value Clipping

  •    Python

This repository contains a collection of scripts used in the experiments of Temporal Generative Adversarial Nets with Singular Value Clipping. Disclaimer: PFN provides no warranty or support for this implementation. Use it at your own risk. See license for details.

SPIRAL-tensorflow - in progress

  •    Python

TensorFlow implementation of Synthesizing Programs for Images using Reinforced Adversarial Learning (SPIRAL). SPIRAL is an adversarially trained agent that generates a program which is executed by a graphics engine to interpret and sample images. This agent is trained to fool a discriminator with a distributed reinforcement learning without any supervision.

gail-tf - Tensorflow implementation of generative adversarial imitation learning

  •    Python

The trained model will save in ./checkpoint, and its precise name will vary based on your optimization method and environment ID. Choose the last checkpoint in the series. Note: The following hyper-parameter setting is the best that I've tested (simple grid search on setting with 1500 trajectories), just for your information.

Img2Img-Translation-Networks - Tensorflow implementation of paper "unsupervised image to image translation networks"

  •    Python

This is the TensorFlow Implementation of the NIPS 2017 paper "Unsupervised Image to Image Translation Networks" by Harry Yang. Disclaimer: This was our own research project but it shares the same idea with the paper so we are making the code publicly available.

sgan - Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018

  •    Python

Human motion is interpersonal, multimodal and follows social conventions. In this paper, we tackle this problem by combining tools from sequence prediction and generative adversarial networks: a recurrent sequence-to-sequence model observes motion histories and predicts future behavior, using a novel pooling mechanism to aggregate information across people. Below we show an examples of socially acceptable predictions made by our model in complex scenarios. Each person is denoted by a different color. We denote observed trajectory by dots and predicted trajectory by stars.

markov-chain-gan - Code for "Generative Adversarial Training for Markov Chains" (ICLR 2017 Workshop)

  •    Python

TensorFlow code for Generative Adversarial Training for Markov Chains (ICLR 2017 Workshop Track). Work by Jiaming Song, Shengjia Zhao and Stefano Ermon.

a-nice-mc - Code for "A-NICE-MC: Adversarial Training for MCMC"

  •    Jupyter

Tensorflow implementation for the paper A-NICE-MC: Adversarial Training for MCMC, NIPS 2017. A-NICE-MC is a framework that trains a parametric Markov Chain Monte Carlo proposal. It achieves higher performance than traditional nonparametric proposals, such as Hamiltonian Monte Carlo (HMC). This repository provides code to replicate the experiments, as well as providing grounds for further research.

lagvae - Lagrangian VAE

  •    Python

TensorFlow implementation for the paper A Lagrangian Perspective of Latent Variable Generative Models, UAI 2018 Oral. Lagrangian VAE provides a practical way to find the best trade-off between "consistency constraints" and "mutual information objectives", as opposed of performing extensive hyperparameter tuning. We demonstrate an example over InfoVAE, a latent variable generative model objective that requires tuning the strengths of corresponding hyperparameters.