{"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/attending-to-discriminative-certainty-for-1","title":"Attending to Discriminative Certainty for Domain Adaptation","arxiv_id":"1906.03502","date":"2019-06-08","proceeding":"CVPR 2019 6","authors":["Vinod Kumar Kurmi","Shanu Kumar","Vinay P. Namboodiri"],"abstract":"In this paper, we aim to solve for unsupervised domain adaptation of classifiers where we have access to label information for the source domain while these are not available for a target domain. While various methods have been proposed for solving these including adversarial discriminator based methods, most approaches have focused on the entire image based domain adaptation. In an image, there would be regions that can be adapted better, for instance, the foreground object may be similar in nature. To obtain such regions, we propose methods that consider the probabilistic certainty estimate of various regions and specify focus on these during classification for adaptation. We observe that just by incorporating the probabilistic certainty of the discriminator while training the classifier, we are able to obtain state of the art results on various datasets as compared against all the recent methods. We provide a thorough empirical analysis of the method by providing ablation analysis, statistical significance test, and visualization of the attention maps and t-SNE embeddings. These evaluations convincingly demonstrate the effectiveness of the proposed approach.","url_abs":"https://arxiv.org/abs/1906.03502v2","url_pdf":"https://arxiv.org/pdf/1906.03502v2.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":"attending-to-discriminative-certainty-for-1","repo_url":"https://github.com/DelTA-Lab-IITK/CADA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/domain-adaptation-on-imageclef-da","task":"Domain Adaptation","dataset":"ImageCLEF-DA","model":"CADA-P","rank_in_archive_order":12,"of":17,"metrics":{"Accuracy":"88.3"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-office-31","task":"Domain Adaptation","dataset":"Office-31","model":"CADA-P","rank_in_archive_order":19,"of":40,"metrics":{"Average Accuracy":"89.5"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-office-home","task":"Domain Adaptation","dataset":"Office-Home","model":"CADA","rank_in_archive_order":23,"of":29,"metrics":{"Accuracy":"70.2"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1906.03502","atlas_url":"https://app.syntology.ai/?focus=1906.03502","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}