GAN implementations collection resource

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17446 stars · 4063 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A collection of clean PyTorch (and companion Keras) implementations of dozens of Generative Adversarial Network variants from research papers, such as CycleGAN, Pix2Pix, WGAN-GP, and InfoGAN. Each implementation focuses on the core ideas of the paper rather than exact architecture reproduction, making it a reference for learning and experimentation.

Use cases

• learn how GAN architectures work with readable pytorch code
• find a reference implementation of cyclegan or pix2pix
• compare different gan variants like wgan-gp and infogan
• get a starting point for implementing a generative adversarial network
• study gan paper implementations for a course or research project

When to choose

• you want simple, readable implementations of many GAN variants in one place
• you are learning generative models and want paper-to-code references
• you need a baseline implementation to modify for your own experiments

When to avoid

• you need a production-ready, actively maintained GAN training framework
• you require exact reproductions of paper architectures or state-of-the-art results
• you need timely bug fixes or support for the latest PyTorch versions

Facets

learning-resource · maturity maintenance

machine-learning deep-learning image-processing deep-learning machine-learning image-processing tutorials python cross-platform gan pytorch keras generative-models reference-implementations research-papers

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For agents

markdown · JSON · MCP: product_card(name="eriklindernoren/PyTorch-GAN")