LSTM-based GAN for simulating DNA sequence evolution
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Updated
Aug 24, 2022 - Python
LSTM-based GAN for simulating DNA sequence evolution
Image to Image translation using conditional GANs with Wasserstein loss and gradient penalty
Tensorflow implementation for training GANs with various objectives and gradient penalties, different network architectures, both image and word generations
Major GANs are implemented in this repository 🔥
PyTorch implementation of 'PGGAN' (Karras et al., 2018) from scratch and training it on CelebA-HQ at 512 × 512
GANs: Losses, Regularizations and Normalizations
My version of cWGAN-gp. Simply my cDCGAN-based but using the Wasserstein Loss and gradient penalty.
A conditional Wasserstein Generative Adversarial Network with gradient penalty (cWGAN-GP) for stochastic generation of galaxy properties in wide-field surveys
Generalized Loss-Sensitive Generative Adversarial Networks (GLS-GAN) in PyTorch with gradient penalty, including both LS-GAN and WGAN as special cases.
Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"
Pytorch implementation of Wasserstein GANs with Gradient Penalty
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