
gan · GitHub Topics · GitHub
Nov 20, 2025 · gan Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the …
GitHub - eriklindernoren/PyTorch-GAN: PyTorch implementations …
Softmax GAN is a novel variant of Generative Adversarial Network (GAN). The key idea of Softmax GAN is to replace the classification loss in the original GAN with a softmax cross …
The GAN is dead; long live the GAN! A Modern Baseline GAN …
Code for NeurIPS 2024 paper - The GAN is dead; long live the GAN! A Modern Baseline GAN - by Huang et al. - brownvc/R3GAN
GitHub - poloclub/ganlab: GAN Lab: An Interactive, Visual ...
GAN Lab is a novel interactive visualization tool for anyone to learn and experiment with Generative Adversarial Networks (GANs), a popular class of complex deep learning models. …
GitHub - Yangyangii/GAN-Tutorial: Simple Implementation of …
Simple Implementation of many GAN models with PyTorch. - Yangyangii/GAN-Tutorial
GitHub - tkarras/progressive_growing_of_gans: Progressive …
The Progressive GAN code repository contains a command-line tool for recreating bit-exact replicas of the datasets that we used in the paper. The tool also provides various utilities for …
generative-adversarial-network · GitHub Topics · GitHub
May 18, 2024 · Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the …
US-GAN: On the importance of Ultimate Skip Connection for Facial ...
This leads to a light-weight US-GAN model comprised of encoding layers, a single residual block, decoding layers, and an ultimate skip connection from input to output. US-GAN has 3x fewer …
PyTorch Pretrained GANs - GitHub
Apr 11, 2021 · Each type of GAN is contained in its own folder and has a make_GAN_TYPE function. For example, make_bigbigan creates a BigBiGAN with the format of the …
GitHub - yfeng95/GAN: Resources and Implementations of …
Wasserstein GAN stabilize the training by using Wasserstein-1 distance GAN before using JS divergence has the problem of non-overlapping, leading to mode collapse and convergence …