igul222/improved_wgan_training

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2411 stars · 661 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Reference implementation of the 'Improved Training of Wasserstein GANs' (WGAN-GP) paper, providing TensorFlow code to reproduce its experiments. It includes training scripts for toy datasets, MNIST, CIFAR-10, 64x64 images, and character-level language modeling.

Use cases

• reproduce WGAN-GP paper experiments
• train a Wasserstein GAN with gradient penalty on MNIST
• compare GAN loss functions on toy datasets like 8 Gaussians and Swiss Roll
• learn how to implement gradient penalty in TensorFlow
• train a GAN on CIFAR-10 or 64x64 images
• experiment with GANs for character-level language modeling

When to choose

• you want to reproduce or verify the WGAN-GP paper's results
• you need a clear reference implementation of the gradient penalty technique
• you are studying GAN training stability with simple, readable TensorFlow code

When to avoid

• you need a production-ready or actively maintained GAN framework
• you use PyTorch or modern TensorFlow 2.x APIs
• you need flexible configuration, checkpointing, or multi-GPU support out of the box

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning deep-learning artificial-intelligence python wgan gan wasserstein gradient-penalty tensorflow research-code generative-models linux gpu

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