Mar 31, 2017 · Our proposed method performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning, including 101-layer …

We propose gradient penalty (WGAN-GP), which does not suffer from the same problems. We demonstrate stable training of varied GAN architectures, performance improvements over weight …

Wasserstein GAN, Martin Arjovsky, Soumith Chintala, Léon Bottou, 2017 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1701.07875 - This paper introduced the …

Paper: https://arxiv.org/pdf/1701.07875.pdf. Implementation of a Wasserstein Generative Adversarial Network with Gradient Penalty to enforce lipchitz constraint. The WGAN utilizes the wasserstein loss …

We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, …

Jan 26, 2017 · We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode …

One of the breakthroughs was WGAN paper (https://arxiv.org/abs/1701.07875). Rather than finding the equilibrium between two neural networks, WGAN paper tries to minimize the 1-Wasserstein Distance …

This repo contains the model and the notebook to this this Keras example on WGAN. The original Wasserstein GAN leverages the Wasserstein distance to produce a value function that has better …

Our proposed method performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning, including 101-layer ResNets and …

This paper presents an improved training method for Wasserstein GANs, enhancing stability and performance in generative adversarial networks.