{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pros-and-cons-of-gan-evaluation-measures","title":"Pros and Cons of GAN Evaluation Measures","arxiv_id":"1802.03446","date":"2018-02-09","proceeding":null,"authors":["Ali Borji"],"abstract":"Generative models, in particular generative adversarial networks (GANs), have\nreceived significant attention recently. A number of GAN variants have been\nproposed and have been utilized in many applications. Despite large strides in\nterms of theoretical progress, evaluating and comparing GANs remains a daunting\ntask. While several measures have been introduced, as of yet, there is no\nconsensus as to which measure best captures strengths and limitations of models\nand should be used for fair model comparison. As in other areas of computer\nvision and machine learning, it is critical to settle on one or few good\nmeasures to steer the progress in this field. In this paper, I review and\ncritically discuss more than 24 quantitative and 5 qualitative measures for\nevaluating generative models with a particular emphasis on GAN-derived models.\nI also provide a set of 7 desiderata followed by an evaluation of whether a\ngiven measure or a family of measures is compatible with them.","url_abs":"http://arxiv.org/abs/1802.03446v5","url_pdf":"http://arxiv.org/pdf/1802.03446v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pros-and-cons-of-gan-evaluation-measures","repo_url":"https://github.com/Jeff-HOU/ML_Note","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"pros-and-cons-of-gan-evaluation-measures","repo_url":"https://github.com/suhitd1729/Action-cameras-evaluating-image-quality-and-suitability-for-machine-learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"pros-and-cons-of-gan-evaluation-measures","repo_url":"https://github.com/suhitd1729/actioncameraforml","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"pros-and-cons-of-gan-evaluation-measures","repo_url":"https://github.com/taki0112/GAN_Metrics-Tensorflow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1802.03446","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.03446"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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