Open-source alternatives to VariationalRecurrentNeuralNetwork

A pytorch implementation of Paper "Improved Training of Wasserstein GANs"

pytorch implementation of fast-neural-style

Code for the paper "Adversarial Generator-Encoder Networks"

This project is a deep learning framework designed for training and deploying image-to-image translation models. It serves as a research platform for experimenting with neural network architectures that transform visual content between distinct stylistic domains, supporting both paired and unpaired training data. The framework distinguishes itself through its support for cycle-consistency constraints, which allow for image translation between domains without requiring corresponding paired examples. It provides a structured pipeline that utilizes adversarial loss optimization, where generator

simple generative adversarial network (GAN) using PyTorch

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

PyTorch implementation of the Quasi-Recurrent Neural Network - up to 16 times faster than NVIDIA's cuDNN LSTM

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

Fast Neural Style for Image Style Transform by Pytorch

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

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

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.

Wasserstein GAN

Sequence to Sequence Models with 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

This code aims to reproduce results obtained in the paper "Visual Feature Attribution using Wasserstein GANs" (official repo, TensorFlow code)

PyTorch Implementation of ReSeg (https://arxiv.org/pdf/1511.07053.pdf)