Open-source alternatives to Visual Feature Attribution Using Wasserstein GANs 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

Code for PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning

Code for the paper "Adversarial Generator-Encoder Networks"

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.

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

Pytorch implementation of CoordConv introduced in An intriguing failing of convolutional neural networks and the CoordConv solution paper

Fast Neural Style for Image Style Transform by Pytorch

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

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

pytorch implementation of fast-neural-style

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

⚠️ Regrettably, I cannot perform maintenance due to the loss of the materials. I'm archiving this repository for reference

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

3D ResNets for Action Recognition (CVPR 2018)

simple generative adversarial network (GAN) using PyTorch

Wasserstein GAN

Code for the paper "Adversarially Regularized Autoencoders (ICML 2018)" by Zhao, Kim, Zhang, Rush and LeCun