Pytorch implementation of Wasserstein GANs with Gradient Penalty
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Dec 4, 2020 - Python
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gradient-penalty
Here are 19 public repositories matching this topic...
Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"
pytorch generative-adversarial-network wasserstein-gan gradient-penalty time-series-anomaly-detection
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Sep 30, 2021 - Python
Generalized Loss-Sensitive Generative Adversarial Networks (GLS-GAN) in PyTorch with gradient penalty, including both LS-GAN and WGAN as special cases.
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Mar 4, 2018 - Python
A conditional Wasserstein Generative Adversarial Network with gradient penalty (cWGAN-GP) for stochastic generation of galaxy properties in wide-field surveys
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Aug 19, 2022 - Python
My version of cWGAN-gp. Simply my cDCGAN-based but using the Wasserstein Loss and gradient penalty.
machine-learning deep-learning gan wasserstein-gan gradient-penalty conditional-gan wasserstein-distance
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Jun 19, 2022 - Python
Keras implementation of WGAN GP for face generation. The model is trained on CelebA dataset.
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Oct 2, 2020 - Jupyter Notebook
GANs: Losses, Regularizations and Normalizations
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Sep 5, 2018 - Python
PyTorch implementation of 'PGGAN' (Karras et al., 2018) from scratch and training it on CelebA-HQ at 512 × 512
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Nov 26, 2024 - Python
Wasserstein GAN with Gradient Penalty in DL4S
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Apr 19, 2021 - Swift
Generating shoes with GANs in sake of lulz and education
lasagne theano deep-learning generative-adversarial-network gan wgan wasserstein-gan gradient-penalty
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Aug 31, 2017 - Jupyter Notebook
Major GANs are implemented in this repository 🔥
tensorflow gan neural-networks gans wasserstein-gan gradient-penalty wasserstein-gans progressive-gan eager-execution tf-gans
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Jan 3, 2020 - Python
Tensorflow implementation for training GANs with various objectives and gradient penalties, different network architectures, both image and word generations
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Dec 7, 2022 - Python
A brief visualization of how GP(Gradient Penalty) for GAN works
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Apr 20, 2019 - Jupyter Notebook
Image to Image translation using conditional GANs with Wasserstein loss and gradient penalty
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Sep 15, 2022 - Python
A collaborative mini-research project analyzing Wasserstein GANs (WGANs) through extensive literature review and experimental evaluation. Explores training stability, loss behavior, gradient penalties, and convergence characteristics, proposing insights to improve generative model robustness.
machine-learning research ai deep-learning pytorch gan neural-networks representation-learning unsupervised-learning wgan generative-models gradient-penalty ai-research wasserstein-distance theoretical-ml model-stability
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Jan 4, 2026
LSTM-based GAN for simulating DNA sequence evolution
lstm generative-adversarial-network evolution-simulation dna-sequences gradient-penalty wasserstein-loss
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Aug 24, 2022 - Python
generative-adversarial-network mnist dcgan mnist-classification wgan lsgan wgan-gp gradient-penalty wassertein-gan loss-sensitive-gan
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Apr 19, 2018 - Python
tabular-data generative-adversarial-network wasserstein-gan gradient-penalty synthetic-data-generation
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Sep 29, 2025 - Python
This project demonstrates a GAN built with PyTorch, using a subset of 5000 CelebA images. It leverages Wasserstein GAN with Gradient Penalty (WGAN-GP) for facial image generation. The provided models are trained for 200 epochs, showcasing integration of techniques from key research papers. Deeper Networks and more Training can improve results.
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Sep 2, 2024 - Jupyter Notebook
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