goodfeli/adversarial

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

4078 stars · 1098 forks observed · 2026-08-28

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

Reference implementation of the original 2014 'Generative Adversarial Networks' paper by Goodfellow et al., containing training code, YAML configs, and hyperparameters built on Theano and Pylearn2. It is academic research code with no support, unit tests, or maintenance.

Use cases

• reproduce the original GAN paper experiments
• study the first GAN implementation and its hyperparameters
• compare modern GAN implementations against the original
• learn how GANs were trained in 2014 with Theano and Pylearn2
• estimate model log-likelihood with the Parzen density technique

When to choose

• you need the exact original GAN code and hyperparameters for citation or historical study
• you are doing research on the provenance or reproduction of the original GAN paper

When to avoid

• you want to train GANs in production or on modern hardware
• you need maintained, documented, or tested software
• your project uses PyTorch, TensorFlow, or JAX instead of Theano and Pylearn2

Facets

library · maturity abandoned

machine-learning deep-learning machine-learning deep-learning artificial-intelligence python generative-adversarial-networks gan research-code theano pylearn2 reference-implementation gpu

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