{"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/geometry-score-a-method-for-comparing","title":"Geometry Score: A Method For Comparing Generative Adversarial Networks","arxiv_id":"1802.02664","date":"2018-02-07","proceeding":"ICML 2018 7","authors":["Valentin Khrulkov","Ivan Oseledets"],"abstract":"One of the biggest challenges in the research of generative adversarial\nnetworks (GANs) is assessing the quality of generated samples and detecting\nvarious levels of mode collapse. In this work, we construct a novel measure of\nperformance of a GAN by comparing geometrical properties of the underlying data\nmanifold and the generated one, which provides both qualitative and\nquantitative means for evaluation. Our algorithm can be applied to datasets of\nan arbitrary nature and is not limited to visual data. We test the obtained\nmetric on various real-life models and datasets and demonstrate that our method\nprovides new insights into properties of GANs.","url_abs":"http://arxiv.org/abs/1802.02664v3","url_pdf":"http://arxiv.org/pdf/1802.02664v3.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":"geometry-score-a-method-for-comparing","repo_url":"https://github.com/KhrulkovV/geometry-score","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"geometry-score-a-method-for-comparing","repo_url":"https://github.com/aimagelab/hwd","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.02664","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.02664"}},"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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