{"id":122807,"date":"2021-11-19T16:45:51","date_gmt":"2021-11-19T07:45:51","guid":{"rendered":"http:\/\/175.125.95.178\/ai-in-computer\/22807\/"},"modified":"2026-04-18T22:55:20","modified_gmt":"2026-04-18T13:55:20","slug":"22807","status":"publish","type":"ai-in-computer","link":"http:\/\/ee.presscat.kr\/en\/ai-in-computer\/22807\/","title":{"rendered":"Hai Tran, Sumyeong Ahn, Taeyoung Lee, and Yung Yi &quot;Enlarging Discriminative Power by Adding an Extra Class in Unsupervised Domain Adaptation&quot;  ICPR (International Conference on Pattern Recognition), 2020."},"content":{"rendered":"<p style=\"text-align:justify;margin-bottom:11px\"><span style=\"font-size:10pt\"><span style=\"line-height:107%\"><span><span lang=\"EN-US\" style=\"line-height:107%\">We study the problem of unsupervised domain adaptation that aims at obtaining a prediction model for the target domain using labeled data from the source domain and unlabeled data from the target domain. There exists an array of recent research based on the idea of extracting features that are not only invariant for both domains but also provide high discriminative power for the target domain.<\/span><\/span><\/span><\/span><\/p>\n<p style=\"text-align:justify;margin-bottom:11px\"><span style=\"font-size:10pt\"><span style=\"line-height:107%\"><span><span lang=\"EN-US\" style=\"line-height:107%\">In this paper, we propose an idea of improving the discriminativeness: Adding an extra artificial class and training the model on the given data together with the GAN-generated samples of the new class.<\/span><\/span><\/span><\/span><\/p>\n<p style=\"text-align:justify;margin-bottom:11px\"><span style=\"font-size:10pt\"><span style=\"line-height:107%\"><span><span lang=\"EN-US\" style=\"line-height:107%\">The trained model based on the new class samples is capable of extracting the features that are more discriminative by repositioning data of current classes in the target domain and therefore<\/span><\/span><\/span><\/span><\/p>\n<p style=\"text-align:justify;margin-bottom:11px\"><span style=\"font-size:10pt\"><span style=\"line-height:107%\"><span><span lang=\"EN-US\" style=\"line-height:107%\">increasing the distances among the target clusters in the feature space. Our idea is highly generic so that it is compatible with many existing methods such as DANN, VADA, and DIRT-T.<\/span><\/span><\/span><\/span><\/p>\n<p style=\"text-align:justify;margin-bottom:11px\"><span style=\"font-size:10pt\"><span style=\"line-height:107%\"><span><span lang=\"EN-US\" style=\"line-height:107%\">We conduct various experiments for the standard data commonly used for the evaluation of unsupervised domain adaptations and demonstrate that our algorithm achieves the SOTA performance for many scenarios.<\/span><\/span><\/span><\/span><\/p>\n<p style=\"text-align:justify;margin-bottom:11px\"><span style=\"font-size:10pt\"><span style=\"line-height:107%\"><span><span lang=\"EN-US\" style=\"line-height:107%\"><\/p>\n<div class=\"\"><img decoding=\"async\" class=\"\" src=\"\/wp-content\/uploads\/drupal\/\uc774\uc735\uad50\uc218\ub2d83.png\" alt=\"\" title=\"\"><\/div>\n<p><\/span><\/span><\/span><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>831<\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-122807","ai-in-computer","type-ai-in-computer","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/ai-in-computer\/122807","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/ai-in-computer"}],"about":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/types\/ai-in-computer"}],"wp:attachment":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/media?parent=122807"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}