# yfeng95/GAN

Resources and Implementations of  Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN

Repository: https://github.com/yfeng95/GAN
Canonical: https://ross.abutalabs.com/products/gan
Language: Python
License Family: other
Topics: gan, dcgan, wasserstein-gan, infogan, adversarial-nets
Last push: 2017-09-08T03:23:21+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3446, "days_push": 3281, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3098, forks 794 (observed 2026-08-28T04:07:43.451025+00:00)

## What it is
A collection of simple, educational implementations of classic Generative Adversarial Networks (GAN, DCGAN, WGAN, CGAN, InfoGAN) in Python with TensorFlow, tested on MNIST. It is organized for learning how GAN math and loss functions translate into actual code.

## Use cases
- learn how GANs are implemented in code
- understand DCGAN architecture on MNIST
- study Wasserstein GAN loss implementation
- compare conditional GAN training strategies
- see how InfoGAN disentangled representations work
- find papers and blogs about adversarial nets

## When to choose
- you want minimal, readable GAN implementations for study
- you are learning GAN theory alongside practical TensorFlow code
- you want to experiment with classic GAN variants on MNIST

## When to avoid
- you need production-ready or maintained GAN code
- you require modern frameworks like PyTorch or recent GAN variants
- you need a supported library with a license and active maintenance

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: machine-learning, deep-learning, image-processing
- domain: deep-learning, machine-learning, computer-vision, tutorials
- platform: python
- tags: gan, dcgan, wgan, cgan, infogan, tensorflow, mnist, educational, linux

## Member repositories
- yfeng95/GAN (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:43.451025+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:27:16.543518+00:00, confidence not recorded.
  - readme: https://github.com/yfeng95/GAN (fetched 2026-08-28T04:07:43.451025+00:00, sha 55bccd21ac5e)
- Data as of 2026-08-30T08:39:29.467469+00:00.