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openai/InfoGAN

Code for reproducing key results in the paper "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets" observed · 2026-08-28

github.com/openai/InfoGAN · homepage · Python · archived observed · 2026-08-28

Health v2 · maintenance only

10/100

  • Activity 0
  • Release rhythm 35
  • Longevity 100

Flags: no_releases archived no_license

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 3744
  • days_rel: n/a
  • days_push: 1987
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

1069 stars · 301 forks observed · 2026-08-28

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

Official research code from OpenAI for reproducing results of the InfoGAN paper, an information-theoretic extension to GANs that learns disentangled representations in an unsupervised manner. It includes an MNIST experiment launcher built on an old dev version of TensorFlow.

Use cases

  • reproduce InfoGAN paper results on MNIST
  • study unsupervised disentangled representation learning
  • explore GAN variants with mutual information objectives
  • reference implementation of information-maximizing GANs
  • learn how research GAN code is structured in TensorFlow

When to choose

  • you need the exact reference implementation of the InfoGAN paper
  • you are studying or extending the original InfoGAN method
  • you want historical research code for academic citation or comparison

When to avoid

  • you need a maintained, production-ready GAN library
  • you want to run on modern TensorFlow or PyTorch versions
  • you need a permissively licensed codebase (no license is provided)
  • you want to train on datasets beyond the provided MNIST example without significant rework

Facets

library · maturity abandoned

machine-learning deep-learning machine-learning deep-learning artificial-intelligence python gan infogan research-code paper-reproduction tensorflow unsupervised-learning representation-learning archived docker

6 sources

Member repositories

RepositoryRoleHealth v2
openai/InfoGANmain10

For agents

markdown · JSON · MCP: product_card(name="openai/InfoGAN")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem