{"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/autodial-automatic-domain-alignment-layers","title":"AutoDIAL: Automatic DomaIn Alignment Layers","arxiv_id":"1704.08082","date":"2017-04-26","proceeding":"ICCV 2017 10","authors":["Fabio Maria Carlucci","Lorenzo Porzi","Barbara Caputo","Elisa Ricci","Samuel Rota Bulò"],"abstract":"Classifiers trained on given databases perform poorly when tested on data\nacquired in different settings. This is explained in domain adaptation through\na shift among distributions of the source and target domains. Attempts to align\nthem have traditionally resulted in works reducing the domain shift by\nintroducing appropriate loss terms, measuring the discrepancies between source\nand target distributions, in the objective function. Here we take a different\nroute, proposing to align the learned representations by embedding in any given\nnetwork specific Domain Alignment Layers, designed to match the source and\ntarget feature distributions to a reference one. Opposite to previous works\nwhich define a priori in which layers adaptation should be performed, our\nmethod is able to automatically learn the degree of feature alignment required\nat different levels of the deep network. Thorough experiments on different\npublic benchmarks, in the unsupervised setting, confirm the power of our\napproach.","url_abs":"http://arxiv.org/abs/1704.08082v3","url_pdf":"http://arxiv.org/pdf/1704.08082v3.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":"autodial-automatic-domain-alignment-layers","repo_url":"https://github.com/ducksoup/autodial","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"autodial-automatic-domain-alignment-layers","repo_url":"https://github.com/2023-MindSpore-1/ms-code-220/tree/main/autodis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":{"status":"ok"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1704.08082","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}