{"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/bias-and-generalization-in-deep-generative","title":"Bias and Generalization in Deep Generative Models: An Empirical Study","arxiv_id":"1811.03259","date":"2018-11-08","proceeding":"NeurIPS 2018 12","authors":["Shengjia Zhao","Hongyu Ren","Arianna Yuan","Jiaming Song","Noah Goodman","Stefano Ermon"],"abstract":"In high dimensional settings, density estimation algorithms rely crucially on\ntheir inductive bias. Despite recent empirical success, the inductive bias of\ndeep generative models is not well understood. In this paper we propose a\nframework to systematically investigate bias and generalization in deep\ngenerative models of images. Inspired by experimental methods from cognitive\npsychology, we probe each learning algorithm with carefully designed training\ndatasets to characterize when and how existing models generate novel attributes\nand their combinations. We identify similarities to human psychology and verify\nthat these patterns are consistent across commonly used models and\narchitectures.","url_abs":"http://arxiv.org/abs/1811.03259v1","url_pdf":"http://arxiv.org/pdf/1811.03259v1.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":"bias-and-generalization-in-deep-generative","repo_url":"https://github.com/ermongroup/BiasAndGeneralization","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"bias-and-generalization-in-deep-generative","repo_url":"https://github.com/rakhimovv/GenerativeLatentFlow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"density-estimation","task_name":"Density Estimation"},{"task_slug":"inductive-bias","task_name":"Inductive Bias"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.03259","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.03259"}},"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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