{"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/multi-domain-adversarial-learning-1","title":"Multi-Domain Adversarial Learning","arxiv_id":"1903.09239","date":"2019-03-21","proceeding":"ICLR 2019 5","authors":["Alice Schoenauer-Sebag","Louise Heinrich","Marc Schoenauer","Michele Sebag","Lani F. Wu","Steve J. Altschuler"],"abstract":"Multi-domain learning (MDL) aims at obtaining a model with minimal average\nrisk across multiple domains. Our empirical motivation is automated microscopy\ndata, where cultured cells are imaged after being exposed to known and unknown\nchemical perturbations, and each dataset displays significant experimental\nbias. This paper presents a multi-domain adversarial learning approach, MuLANN,\nto leverage multiple datasets with overlapping but distinct class sets, in a\nsemi-supervised setting. Our contributions include: i) a bound on the average-\nand worst-domain risk in MDL, obtained using the H-divergence; ii) a new loss\nto accommodate semi-supervised multi-domain learning and domain adaptation;\niii) the experimental validation of the approach, improving on the state of the\nart on two standard image benchmarks, and a novel bioimage dataset, Cell.","url_abs":"http://arxiv.org/abs/1903.09239v1","url_pdf":"http://arxiv.org/pdf/1903.09239v1.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":"multi-domain-adversarial-learning-1","repo_url":"https://github.com/AltschulerWu-Lab/MuLANN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"}],"methods":[{"method_slug":"mdl","method_name":"MDL"}],"datasets_introduced":[{"slug":"cell","name":"Cell","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.09239","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}