Open-source alternatives to Wgan Gp

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

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

Collection of generative models in Pytorch version.

Wasserstein GAN

The author's officially unofficial PyTorch BigGAN implementation.

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

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

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

PyTorch-GAN is a research-oriented framework providing a collection of modular implementations for generative adversarial network architectures. It serves as a toolkit for training and evaluating models that utilize adversarial minimax optimization to produce synthetic data, offering a structured environment for exploring complex generative tasks within the PyTorch ecosystem. The library distinguishes itself through a comprehensive suite of image synthesis and manipulation capabilities, including super-resolution, inpainting, and cross-domain style translation. It supports advanced training m

PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"

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

This project is a PyTorch object detection framework that implements the Faster R-CNN architecture. It serves as a vision model for predicting precise bounding boxes around multiple objects within images and live video feeds. The system is optimized for multi-GPU training to reduce the time required for model convergence. It utilizes a GPU-accelerated design to handle the training and inference of complex detection networks. The framework covers the full object detection lifecycle, including custom network training and inference for static images and real-time video streams. It includes capa

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.

Image Deblurring using Generative Adversarial Networks

pytorch implementation of fast-neural-style

Fast Neural Style for Image Style Transform by Pytorch