gan Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network. GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training ...
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 sample space of one single batch.
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 Well-tested examples Interactive introduction to TF-GAN in
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 can interactively train GAN models for 2D data distributions and visualize their inner-workings ...
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 datasets:
Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network. GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training dataset.
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 difficulty. Use EM distance or Wasserstein-1 distance, so GAN solve the two problems above without particular architecture (like dcgan).
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. The weights of all GANs except those in PyTorch-StudioGAN and are downloaded automatically. To download the PyTorch-StudioGAN weights, use the download.sh scripts in the corresponding folders (see the file structure below).
