martinarjovsky/WassersteinGAN

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3243 stars · 722 forks observed · 2026-08-28

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

Reference PyTorch implementation of the Wasserstein GAN paper, providing training scripts for DCGAN and MLP architectures on datasets like LSUN. It is research code accompanying the arXiv paper rather than a reusable library.

Use cases

• reproduce Wasserstein GAN experiments from the paper
• train a WGAN on the LSUN dataset
• compare Wasserstein loss curves with DCGAN baselines
• learn how to implement WGAN critic and generator training in PyTorch
• study improved GAN training stability techniques

When to choose

• you want the official reference code for the Wasserstein GAN paper
• you need to reproduce the paper's LSUN experiments exactly
• you are studying WGAN training dynamics and loss behavior

When to avoid

• you need a maintained, production-ready GAN library
• you want a flexible framework for many generative model types
• you need Windows support or extensive documentation

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning deep-learning artificial-intelligence python gan wasserstein-gan pytorch research-code generative-models paper-reproduction linux macos gpu

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