{"adoption": {"forks": 4063, "observed_at": "2026-08-28T04:11:19.677304+00:00", "stars": 17446}, "canonical_url": "https://ross.abutalabs.com/products/gan-implementations-collection", "card": {"archived": false, "artifact_type": "learning-resource", "description": "PyTorch implementations of Generative Adversarial Networks.", "domain": ["deep-learning", "machine-learning", "image-processing", "tutorials"], "enriched": true, "function": ["machine-learning", "deep-learning", "image-processing"], "health_score": 20, "homepage": null, "language": "Python", "license": "MIT", "license_family": "permissive", "maturity": "maintenance", "member_repos": ["eriklindernoren/PyTorch-GAN", "eriklindernoren/Keras-GAN"], "name": "GAN implementations collection", "platform": ["python", "cross-platform"], "pushed_at": "2024-06-18T07:08:31+00:00", "repo": "eriklindernoren/PyTorch-GAN", "stars": 17446, "tags": ["gan", "pytorch", "keras", "generative-models", "reference-implementations", "research-papers"], "topics": [], "urls": [], "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"], "what_it_is": "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.", "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"], "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"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/repos/eriklindernoren/PyTorch-GAN", "repo": "eriklindernoren/PyTorch-GAN", "role": "main", "score": 32}, {"path": "/repos/eriklindernoren/Keras-GAN", "repo": "eriklindernoren/Keras-GAN", "role": "mirror", "score": 32}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:11:19.677304+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-29T17:02:57.211995+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "2244f5168c3ae1b0954170faa086e16a6dbd23abb80b6b12078fe0397a5534ed", "fetched_at": "2026-08-28T04:11:19.677304+00:00", "kind": "readme", "missing": false, "url": "https://github.com/eriklindernoren/PyTorch-GAN"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:11:19.677304+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-29T17:02:57.211995+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "2244f5168c3ae1b0954170faa086e16a6dbd23abb80b6b12078fe0397a5534ed", "fetched_at": "2026-08-28T04:11:19.677304+00:00", "kind": "readme", "missing": false, "url": "https://github.com/eriklindernoren/PyTorch-GAN"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-29T17:02:57.211995+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "2244f5168c3ae1b0954170faa086e16a6dbd23abb80b6b12078fe0397a5534ed", "fetched_at": "2026-08-28T04:11:19.677304+00:00", "kind": "readme", "missing": false, "url": "https://github.com/eriklindernoren/PyTorch-GAN"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:11:19.677304+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:11:19.677304+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:11:19.677304+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-29T17:02:57.211995+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "2244f5168c3ae1b0954170faa086e16a6dbd23abb80b6b12078fe0397a5534ed", "fetched_at": "2026-08-28T04:11:19.677304+00:00", "kind": "readme", "missing": false, "url": "https://github.com/eriklindernoren/PyTorch-GAN"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:11:19.677304+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:11:19.677304+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-29T17:02:57.211995+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "2244f5168c3ae1b0954170faa086e16a6dbd23abb80b6b12078fe0397a5534ed", "fetched_at": "2026-08-28T04:11:19.677304+00:00", "kind": "readme", "missing": false, "url": "https://github.com/eriklindernoren/PyTorch-GAN"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:11:19.677304+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:11:19.677304+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:11:19.677304+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-29T17:02:57.211995+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "2244f5168c3ae1b0954170faa086e16a6dbd23abb80b6b12078fe0397a5534ed", "fetched_at": "2026-08-28T04:11:19.677304+00:00", "kind": "readme", "missing": false, "url": "https://github.com/eriklindernoren/PyTorch-GAN"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:11:19.677304+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:11:19.677304+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-29T17:02:57.211995+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "2244f5168c3ae1b0954170faa086e16a6dbd23abb80b6b12078fe0397a5534ed", "fetched_at": "2026-08-28T04:11:19.677304+00:00", "kind": "readme", "missing": false, "url": "https://github.com/eriklindernoren/PyTorch-GAN"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-29T17:02:57.211995+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "2244f5168c3ae1b0954170faa086e16a6dbd23abb80b6b12078fe0397a5534ed", "fetched_at": "2026-08-28T04:11:19.677304+00:00", "kind": "readme", "missing": false, "url": "https://github.com/eriklindernoren/PyTorch-GAN"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-29T17:02:57.211995+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "2244f5168c3ae1b0954170faa086e16a6dbd23abb80b6b12078fe0397a5534ed", "fetched_at": "2026-08-28T04:11:19.677304+00:00", "kind": "readme", "missing": false, "url": "https://github.com/eriklindernoren/PyTorch-GAN"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-29T17:02:57.211995+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "2244f5168c3ae1b0954170faa086e16a6dbd23abb80b6b12078fe0397a5534ed", "fetched_at": "2026-08-28T04:11:19.677304+00:00", "kind": "readme", "missing": false, "url": "https://github.com/eriklindernoren/PyTorch-GAN"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 0, "longevity": 100, "rhythm": 35}, "computed_at": "2026-09-02T17:46:02.011165+00:00", "flags": ["no_releases"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 3056, "days_push": 806, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 32, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}