{"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/exgan-adversarial-generation-of-extreme","title":"ExGAN: Adversarial Generation of Extreme Samples","arxiv_id":"2009.08454","date":"2020-09-17","proceeding":null,"authors":["Siddharth Bhatia","Arjit Jain","Bryan Hooi"],"abstract":"Mitigating the risk arising from extreme events is a fundamental goal with many applications, such as the modelling of natural disasters, financial crashes, epidemics, and many others. To manage this risk, a vital step is to be able to understand or generate a wide range of extreme scenarios. Existing approaches based on Generative Adversarial Networks (GANs) excel at generating realistic samples, but seek to generate typical samples, rather than extreme samples. Hence, in this work, we propose ExGAN, a GAN-based approach to generate realistic and extreme samples. To model the extremes of the training distribution in a principled way, our work draws from Extreme Value Theory (EVT), a probabilistic approach for modelling the extreme tails of distributions. For practical utility, our framework allows the user to specify both the desired extremeness measure, as well as the desired extremeness probability they wish to sample at. Experiments on real US Precipitation data show that our method generates realistic samples, based on visual inspection and quantitative measures, in an efficient manner. Moreover, generating increasingly extreme examples using ExGAN can be done in constant time (with respect to the extremeness probability $\\tau$), as opposed to the $\\mathcal{O}(\\frac{1}{\\tau})$ time required by the baseline approach.","url_abs":"https://arxiv.org/abs/2009.08454v3","url_pdf":"https://arxiv.org/pdf/2009.08454v3.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":"exgan-adversarial-generation-of-extreme","repo_url":"https://github.com/Stream-AD/ExGAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"Extreme Sample Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2009.08454","atlas_url":"https://app.syntology.ai/?focus=2009.08454","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.08454"}},"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/Stream-AD/ExGAN","reach":null}],"summary":{"ran_draft_wrong":3},"by_repo_kind":{"official":{"samples":3,"ran":3,"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":"b76b648ada967054","entry":"convBNReLU","repo":"Stream-AD/ExGAN","repo_kind":"official","path":"ExGAN.py","file_url":"https://github.com/Stream-AD/ExGAN/blob/HEAD/ExGAN.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"b76b648ada967054"}},{"code_sha256_prefix":"11c2f0ac56aba7c2","entry":"convTBNReLU","repo":"Stream-AD/ExGAN","repo_kind":"official","path":"ExGAN.py","file_url":"https://github.com/Stream-AD/ExGAN/blob/HEAD/ExGAN.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"11c2f0ac56aba7c2"}},{"code_sha256_prefix":"eb12190e9a71b5ba","entry":"sample_genpareto","repo":"Stream-AD/ExGAN","repo_kind":"official","path":"ExGAN.py","file_url":"https://github.com/Stream-AD/ExGAN/blob/HEAD/ExGAN.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"eb12190e9a71b5ba"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}