{"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/effectively-unbiased-fid-and-inception-score","title":"Effectively Unbiased FID and Inception Score and where to find them","arxiv_id":"1911.07023","date":"2019-11-16","proceeding":"CVPR 2020 6","authors":["Min Jin Chong","David Forsyth"],"abstract":"This paper shows that two commonly used evaluation metrics for generative models, the Fr\\'echet Inception Distance (FID) and the Inception Score (IS), are biased -- the expected value of the score computed for a finite sample set is not the true value of the score. Worse, the paper shows that the bias term depends on the particular model being evaluated, so model A may get a better score than model B simply because model A's bias term is smaller. This effect cannot be fixed by evaluating at a fixed number of samples. This means all comparisons using FID or IS as currently computed are unreliable. We then show how to extrapolate the score to obtain an effectively bias-free estimate of scores computed with an infinite number of samples, which we term $\\overline{\\textrm{FID}}_\\infty$ and $\\overline{\\textrm{IS}}_\\infty$. In turn, this effectively bias-free estimate requires good estimates of scores with a finite number of samples. We show that using Quasi-Monte Carlo integration notably improves estimates of FID and IS for finite sample sets. Our extrapolated scores are simple, drop-in replacements for the finite sample scores. Additionally, we show that using low discrepancy sequence in GAN training offers small improvements in the resulting generator.","url_abs":"https://arxiv.org/abs/1911.07023v3","url_pdf":"https://arxiv.org/pdf/1911.07023v3.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":"effectively-unbiased-fid-and-inception-score","repo_url":"https://github.com/mchong6/FID_IS_infinity","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1911.07023","atlas_url":"https://app.syntology.ai/?focus=1911.07023","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.07023"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/mchong6/FID_IS_infinity","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":4},"by_repo_kind":{"official":{"samples":4,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"1938147133587a22","entry":"calculate_FID_infinity","repo":"mchong6/FID_IS_infinity","repo_kind":"official","path":"score_infinity.py","file_url":"https://github.com/mchong6/FID_IS_infinity/blob/HEAD/score_infinity.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1938147133587a22"}},{"code_sha256_prefix":"1c83672ee4fd3628","entry":"calculate_FID_infinity_path","repo":"mchong6/FID_IS_infinity","repo_kind":"official","path":"score_infinity.py","file_url":"https://github.com/mchong6/FID_IS_infinity/blob/HEAD/score_infinity.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1c83672ee4fd3628"}},{"code_sha256_prefix":"aadcb9db697e528d","entry":"calculate_IS_infinity","repo":"mchong6/FID_IS_infinity","repo_kind":"official","path":"score_infinity.py","file_url":"https://github.com/mchong6/FID_IS_infinity/blob/HEAD/score_infinity.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"aadcb9db697e528d"}},{"code_sha256_prefix":"28d83c8f5084a8e1","entry":"load_inception_net","repo":"mchong6/FID_IS_infinity","repo_kind":"official","path":"inception.py","file_url":"https://github.com/mchong6/FID_IS_infinity/blob/HEAD/inception.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"28d83c8f5084a8e1"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}