2025年12月15日 · gan Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the …

Code for NeurIPS 2024 paper - The GAN is dead; long live the GAN! A Modern Baseline GAN - by Huang et al. - brownvc/R3GAN

TF-GAN is a lightweight library for training and evaluating Generative Adversarial Networks (GANs). Can be installed with pip using pip install tensorflow-gan, and used with import tensorflow_gan as tfgan …

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-entropy loss in the …

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 operating on the …

This repo contains PyTorch implementation of various GAN architectures. It's aimed at making it easy for beginners to start playing and learning about GANs. All of the repos I found do obscure things like …

2021年4月11日 · 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 GeneratorWrapper above. …

GAN before using JS divergence has the problem of non-overlapping, leading to mode collapse and convergence difficulty. Use EM distance or Wasserstein-1 distance, so GAN solve the two problems …

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. With GAN Lab, you …

Generative denoising diffusion models typically assume that the denoising distribution can be modeled by a Gaussian distribution. This assumption holds only for small denoising steps, which in practice …