Projects sharing features with Wgan Gp

Code for the paper "Adversarial Generator-Encoder Networks"

Collection of generative models in Pytorch version.

simple generative adversarial network (GAN) using PyTorch

Wasserstein GAN

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

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

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

Image Deblurring using Generative Adversarial Networks

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

CycleGAN is a generative adversarial network framework designed for unpaired image-to-image translation. It enables the conversion of images between two distinct visual domains using datasets that do not require direct one-to-one matching examples. The project implements a deep learning style transfer tool capable of artistic style transfer, object transfiguration, and domain-to-domain conversion. It uses a dual-generator architecture and cycle-consistency loss to ensure that images translated to a target domain and back recover their original state. The framework covers core machine learnin

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

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

This is a TensorFlow implementation of the Deep Convolutional Generative Adversarial Network (DCGAN) architecture, providing a framework for training generative models that produce synthetic images from random noise vectors. The project implements the core DCGAN design, using transposed convolutions for upsampling, batch normalization for training stability, and leaky ReLU activations in the discriminator, all executed as static TensorFlow computation graphs. The implementation supports training on custom image datasets by accepting user-supplied image folders without requiring a predefined f

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

The author's officially unofficial PyTorch BigGAN implementation.

Fast Neural Style for Image Style Transform by Pytorch