Pytorch implementation of Wasserstein GANs with Gradient Penalty
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Updated
Dec 4, 2020 - Python
Pytorch implementation of Wasserstein GANs with Gradient Penalty
Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"
Generalized Loss-Sensitive Generative Adversarial Networks (GLS-GAN) in PyTorch with gradient penalty, including both LS-GAN and WGAN as special cases.
A conditional Wasserstein Generative Adversarial Network with gradient penalty (cWGAN-GP) for stochastic generation of galaxy properties in wide-field surveys
My version of cWGAN-gp. Simply my cDCGAN-based but using the Wasserstein Loss and gradient penalty.
GANs: Losses, Regularizations and Normalizations
PyTorch implementation of 'PGGAN' (Karras et al., 2018) from scratch and training it on CelebA-HQ at 512 × 512
Major GANs are implemented in this repository 🔥
Tensorflow implementation for training GANs with various objectives and gradient penalties, different network architectures, both image and word generations
Image to Image translation using conditional GANs with Wasserstein loss and gradient penalty
LSTM-based GAN for simulating DNA sequence evolution
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