5 days ago · Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator …

The key idea of Softmax GAN is to replace the classification loss in the original GAN with a softmax cross-entropy loss in the sample space of one single batch. In the adversarial learning …

TF-GAN is composed of several parts, which are designed to exist independently: Core : the main infrastructure needed to train a GAN. Set up training with any combination of TF-GAN library …

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 …

Simple Implementation of many GAN models with PyTorch. Topics pytorch gan mnist infogan dcgan regularization celeba wgan began wgan-gp infogan-pytorch conditional-gan pytorch …

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, …

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. …

Build a more sophisticated GAN using convolutional layers. Learn about useful activation functions, batch normalization, and transposed convolutions to tune your GAN architecture …

This paper introduces Diffusion-GAN that employs a Gaussian mixture distribution, defined over all the diffusion steps of a forward diffusion chain, to inject instance noise. A random sample …

Apr 11, 2021 · Pretrained GANs in PyTorch: StyleGAN2, BigGAN, BigBiGAN, SAGAN, SNGAN, SelfCondGAN, and more - lukemelas/pytorch-pretrained-gans