Projects sharing features with WassersteinGAN

Code for the paper "Improved Techniques for Training GANs"

Code for "On the Effects of Batch and Weight Normalization in Generative Adversarial Networks"

StarGAN is a PyTorch image-to-image translation framework designed to synthesize visual styles and attributes across multiple domains. It implements a generative adversarial network that serves as a deep learning image translator for modifying specific visual characteristics within an image dataset. The framework uses a single unified model to handle translations between multiple image domains rather than requiring separate pairs of models. It is a research implementation that learns mappings between different image attributes without the need for paired training data. The project covers the

simple generative adversarial network (GAN) using PyTorch

pix2pixHD is a conditional generative adversarial network designed to transform semantic label maps into high-resolution photorealistic images. It functions as a high-resolution image synthesizer and an image-to-image translation model capable of producing synthetic images at 2048x1024 resolution. The system includes a semantic image editor that allows for the modification of high-resolution visuals by updating the underlying semantic label maps. This enables interactive image editing and the generation of photorealistic images based on source images or discrete label maps. The framework pro

PyTorch implementation of Image-to-Image Translation Using Conditional Adversarial Networks.

This project is a generative adversarial network implementation and research framework. It provides the tools and hyperparameters necessary to train and evaluate generative models across various datasets, specifically designed to reproduce results from academic research. The framework includes a Parzen density likelihood estimator to calculate model log likelihood. This allows for the quantitative evaluation of generative distributions and the measurement of overall model performance. The codebase covers machine learning research capabilities, focusing on the training of adversarial networks

We have a reimplementation of the UNIT method that is more performant. It is avaiable at Imaginaire

A list of all named GANs!

A repository for the paper "Improved Adversarial Systems for 3D Object Generation and Reconstruction".

This project is an unsupervised image restoration tool that uses a convolutional neural network as a structural prior to reconstruct images from noisy or incomplete data. It functions as a neural network image prior, utilizing the inherent biases of the network architecture to restore pixels without the need for a pre-trained dataset or external learning. The system performs zero-shot image restoration by treating the network architecture itself as a regularization term. It uses a randomly initialized encoder-decoder structure and iterative gradient descent to minimize pixel-wise loss, recove

Pytorch implementation

A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing.

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

The author's officially unofficial PyTorch BigGAN implementation.

This is a PyTorch implementation of the paper NIMA: Neural IMage Assessment (accepted at IEEE Transactions on Image Processing) by Hossein Talebi and Peyman Milanfar. You can learn more from this post at Google Research Blog.

Fast Neural Style for Image Style Transform by Pytorch