# zhangqianhui/AdversarialNetsPapers

Awesome paper list with code about generative adversarial nets

Repository: https://github.com/zhangqianhui/AdversarialNetsPapers
Canonical: https://ross.abutalabs.com/products/adversarialnetspapers
License Family: other
Topics: adversarial-networks, gan, image-translation, deep-learning
Last push: 2022-10-31T08:39:47+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": 3630, "days_push": 1402, "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 6570, forks 1890 (observed 2026-08-28T04:09:45.314191+00:00)

## What it is
A curated awesome-list of research papers on generative adversarial networks (GANs), organized by application area, theory, and tutorials, with links to code implementations. It is a reference resource rather than runnable software.

## Use cases
- find GAN papers with code
- learn about generative adversarial networks
- research image-to-image translation methods
- survey GAN applications like super-resolution and inpainting
- find tutorials and blogs on GANs
- keep up with adversarial network research

## When to choose
- you need a curated reading list of GAN research with code links
- you are surveying GAN applications across vision tasks
- you want the original GAN paper plus follow-up work organized by topic

## When to avoid
- you need runnable software or a library to train GANs
- you need up-to-date coverage of diffusion models and recent generative AI
- you need a maintained project with a license and active releases

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: deep-learning, machine-learning, image-processing
- domain: deep-learning, computer-vision, artificial-intelligence, awesome-lists
- platform: cross-platform
- tags: gan, awesome-list, papers, generative-models, research

## Member repositories
- zhangqianhui/AdversarialNetsPapers (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:45.314191+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 2026-08-29T17:43:39.578234+00:00, confidence not recorded.
  - readme: https://github.com/zhangqianhui/AdversarialNetsPapers (fetched 2026-08-28T04:09:45.314191+00:00, sha a8034880fef7)
- Data as of 2026-08-30T08:39:29.467469+00:00.