{"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/bourgan-generative-networks-with-metric","title":"BourGAN: Generative Networks with Metric Embeddings","arxiv_id":"1805.07674","date":"2018-05-19","proceeding":"NeurIPS 2018 12","authors":["Chang Xiao","Peilin Zhong","Changxi Zheng"],"abstract":"This paper addresses the mode collapse for generative adversarial networks\n(GANs). We view modes as a geometric structure of data distribution in a metric\nspace. Under this geometric lens, we embed subsamples of the dataset from an\narbitrary metric space into the l2 space, while preserving their pairwise\ndistance distribution. Not only does this metric embedding determine the\ndimensionality of the latent space automatically, it also enables us to\nconstruct a mixture of Gaussians to draw latent space random vectors. We use\nthe Gaussian mixture model in tandem with a simple augmentation of the\nobjective function to train GANs. Every major step of our method is supported\nby theoretical analysis, and our experiments on real and synthetic data confirm\nthat the generator is able to produce samples spreading over most of the modes\nwhile avoiding unwanted samples, outperforming several recent GAN variants on a\nnumber of metrics and offering new features.","url_abs":"http://arxiv.org/abs/1805.07674v3","url_pdf":"http://arxiv.org/pdf/1805.07674v3.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":"bourgan-generative-networks-with-metric","repo_url":"https://github.com/a554b554/BourGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.07674","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.07674"}},"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/a554b554/BourGAN","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"listed":{"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":"4a5d13ea6cb239a6","entry":"loadSampler","repo":"a554b554/BourGAN","repo_kind":"listed","path":"src/bourgan/sampler/BourgainSampler.py","file_url":"https://github.com/a554b554/BourGAN/blob/HEAD/src/bourgan/sampler/BourgainSampler.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"4a5d13ea6cb239a6"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}