{"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/an-improved-evaluation-framework-for","title":"An Improved Evaluation Framework for Generative Adversarial Networks","arxiv_id":"1803.07474","date":"2018-03-20","proceeding":null,"authors":["Shaohui Liu","Yi Wei","Jiwen Lu","Jie zhou"],"abstract":"In this paper, we propose an improved quantitative evaluation framework for\nGenerative Adversarial Networks (GANs) on generating domain-specific images,\nwhere we improve conventional evaluation methods on two levels: the feature\nrepresentation and the evaluation metric. Unlike most existing evaluation\nframeworks which transfer the representation of ImageNet inception model to map\nimages onto the feature space, our framework uses a specialized encoder to\nacquire fine-grained domain-specific representation. Moreover, for datasets\nwith multiple classes, we propose Class-Aware Frechet Distance (CAFD), which\nemploys a Gaussian mixture model on the feature space to better fit the\nmulti-manifold feature distribution. Experiments and analysis on both the\nfeature level and the image level were conducted to demonstrate improvements of\nour proposed framework over the recently proposed state-of-the-art FID method.\nTo our best knowledge, we are the first to provide counter examples where FID\ngives inconsistent results with human judgments. It is shown in the experiments\nthat our framework is able to overcome the shortness of FID and improves\nrobustness. Code will be made available.","url_abs":"http://arxiv.org/abs/1803.07474v3","url_pdf":"http://arxiv.org/pdf/1803.07474v3.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":"an-improved-evaluation-framework-for","repo_url":"https://github.com/B1ueber2y/CAFD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1803.07474","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.07474"}},"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. 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/B1ueber2y/CAFD","reach":null}],"summary":{"ran_fixture":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"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":1,"samples":[{"code_sha256_prefix":"d00c2f40185b1855","entry":"calculate_CAFD","repo":"B1ueber2y/CAFD","repo_kind":"official","path":"cafd.py","file_url":"https://github.com/B1ueber2y/CAFD/blob/HEAD/cafd.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"d00c2f40185b1855"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}