Discriminator-free adversarial domain adaptation with information balance
In the realm of Unsupervised Domain Adaptation (UDA), adversarial learning has achieved significant progress. Existing adversarial UDA methods typically employ additional discriminators and feature extractors to engage in a max-min game. However, these methods often fail to effectively utilize the p...
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| Format: | Article |
| Language: | English |
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AIMS Press
2025-01-01
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| Series: | Electronic Research Archive |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2025011 |
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| author | Hui Jiang Di Wu Xing Wei Wenhao Jiang Xiongbo Qing |
| author_facet | Hui Jiang Di Wu Xing Wei Wenhao Jiang Xiongbo Qing |
| author_sort | Hui Jiang |
| collection | DOAJ |
| description | In the realm of Unsupervised Domain Adaptation (UDA), adversarial learning has achieved significant progress. Existing adversarial UDA methods typically employ additional discriminators and feature extractors to engage in a max-min game. However, these methods often fail to effectively utilize the predicted discriminative information, thus resulting in the mode collapse of the generator. In this paper, we propose a Dynamic Balance-based Domain Adaptation (DBDA) method for self-correlated domain adaptive image classification. Instead of adding extra discriminators, we repurpose the classifier as a discriminator and introduce a dynamic balancing learning approach. This approach ensures an explicit domain alignment and category distinction, thus enabling DBDA to fully leverage the predicted discriminative information for an effective feature alignment. We conducted experiments on multiple datasets, therefore demonstrating that the proposed method maintains a robust classification performance across various scenarios. |
| format | Article |
| id | doaj-art-b7ec14c6425f4dfead9c5c0eb82cef7c |
| institution | OA Journals |
| issn | 2688-1594 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj-art-b7ec14c6425f4dfead9c5c0eb82cef7c2025-08-20T02:26:15ZengAIMS PressElectronic Research Archive2688-15942025-01-0133121023010.3934/era.2025011Discriminator-free adversarial domain adaptation with information balanceHui Jiang0Di Wu1Xing Wei2Wenhao Jiang3Xiongbo Qing4Key Laboratory of Philosophy and Social Science of Anhui Province on Adolescent Mental Health and Crisis Intelligence Intervention, Hefei Normal University, Hefei 230601, ChinaKey Laboratory of Philosophy and Social Science of Anhui Province on Adolescent Mental Health and Crisis Intelligence Intervention, Hefei Normal University, Hefei 230601, ChinaSchool of Computer and Information, Hefei University of Technology, Hefei 230601, ChinaSchool of Computer and Information, Hefei University of Technology, Hefei 230601, ChinaSchool of Computer and Information, Hefei University of Technology, Hefei 230601, ChinaIn the realm of Unsupervised Domain Adaptation (UDA), adversarial learning has achieved significant progress. Existing adversarial UDA methods typically employ additional discriminators and feature extractors to engage in a max-min game. However, these methods often fail to effectively utilize the predicted discriminative information, thus resulting in the mode collapse of the generator. In this paper, we propose a Dynamic Balance-based Domain Adaptation (DBDA) method for self-correlated domain adaptive image classification. Instead of adding extra discriminators, we repurpose the classifier as a discriminator and introduce a dynamic balancing learning approach. This approach ensures an explicit domain alignment and category distinction, thus enabling DBDA to fully leverage the predicted discriminative information for an effective feature alignment. We conducted experiments on multiple datasets, therefore demonstrating that the proposed method maintains a robust classification performance across various scenarios.https://www.aimspress.com/article/doi/10.3934/era.2025011transfer learningdomain adaptationimage classificationdiscriminator-free adversarial learningdynamic balancing |
| spellingShingle | Hui Jiang Di Wu Xing Wei Wenhao Jiang Xiongbo Qing Discriminator-free adversarial domain adaptation with information balance Electronic Research Archive transfer learning domain adaptation image classification discriminator-free adversarial learning dynamic balancing |
| title | Discriminator-free adversarial domain adaptation with information balance |
| title_full | Discriminator-free adversarial domain adaptation with information balance |
| title_fullStr | Discriminator-free adversarial domain adaptation with information balance |
| title_full_unstemmed | Discriminator-free adversarial domain adaptation with information balance |
| title_short | Discriminator-free adversarial domain adaptation with information balance |
| title_sort | discriminator free adversarial domain adaptation with information balance |
| topic | transfer learning domain adaptation image classification discriminator-free adversarial learning dynamic balancing |
| url | https://www.aimspress.com/article/doi/10.3934/era.2025011 |
| work_keys_str_mv | AT huijiang discriminatorfreeadversarialdomainadaptationwithinformationbalance AT diwu discriminatorfreeadversarialdomainadaptationwithinformationbalance AT xingwei discriminatorfreeadversarialdomainadaptationwithinformationbalance AT wenhaojiang discriminatorfreeadversarialdomainadaptationwithinformationbalance AT xiongboqing discriminatorfreeadversarialdomainadaptationwithinformationbalance |