Open-source alternatives to Simulated Unsupervised Tensorflow

Implementation of Sequence Generative Adversarial Nets with Policy Gradient

Caffe

Tensorflow Implementation of the paper "Spectral Normalization for Generative Adversarial Networks" (ICML 2017 workshop)

This project is a command-line tool designed for image super-resolution and noise reduction, with a primary focus on anime-style illustrations. It utilizes convolutional neural network inference to reconstruct missing pixel data and remove digital artifacts, allowing users to upscale images and reduce noise either independently or in a single simultaneous processing pass. Beyond its core image restoration capabilities, the software provides a comprehensive suite for machine learning model training. Users can prepare custom datasets and optimize neural networks for specific restoration tasks,

R-CNN: Regions with Convolutional Neural Network Features

code for Holistically-Nested Edge Detection

The testing code for deep contour detector

MoFA

Randomized Correspondence Algorithm for Structural Image Editing

3D Generative Adversarial Network