Domain-Adversarial Learning

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Overview

Domain-adversarial learning studies how to make models work across domains, such as different hospitals, devices, users, imaging modalities, or time periods. A common approach uses a domain discriminator and gradient reversal to encourage domain-invariant features, often combined with feature alignment, contrastive learning, class prototypes, or pseudo-labeling. These methods are used in supervised and unsupervised adaptation, including settings where target-domain labels are scarce or unavailable. A central tension is that removing domain information can also discard useful task information: alignment may fail under label shift or harm representations that already encode relevant domain knowledge, so invariance does not guarantee better transfer.

Latest papers

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35 papers6 papers in the last four weeks, up 100% on the four weeks before. 0.0% of all new papers.

  1. Boundary-aware Prototype-driven Adversarial Alignment for Cross-Corpus EEG Emotion Recognition

    Mar 18, 2026Guangli Li, Canbiao Wu, Na Tian +2EEG Emotion RecognitionDomain-Adversarial Learning

    School of Biological Science and Medical Engineering, Hunan University of Technology, Zhuzhou 412008, China. · School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China · Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China +1

  2. Robust Generative Audio Quality Assessment: Disentangling Quality from Spurious Correlations

    Mar 17, 2026Kuan-Tang Huang, Chien-Chun Wang, Cheng-Yeh Yang +3Domain GeneralizationDomain-Adversarial Learning

    Dept. Computer Science and Information Engineering, National Taiwan Normal University, Taiwan · E.SUN Financial Holding Co., Ltd., Taiwan · United Link Co., Ltd., Taiwan +1

  3. Adversarial Domain Adaptation Enables Knowledge Transfer Across Heterogeneous RNA-Seq Datasets

    Mar 9, 2026Kevin Dradjat, Massinissa Hamidi, Blaise HanczarCancer Subtype ClassificationDomain Adaptation

    IBISC Laboratory, University Paris-Saclay (Univ. Evry), France · ADLIN, Paris, France

  4. Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy

    Mar 5, 2026Peter Lalor, Ayush Panigrahy, Alex HagenSynthetic-to-Real Domain AdaptationUnsupervised Domain Adaptation

    Pacific Northwest National Laboratory, Richland, WA 99352 USA · University of Washington, Seattle, WA 98195 USA

  5. Embedded Inter-Subject Variability in Adversarial Learning for Inertial Sensor-Based Human Activity Recognition

    Mar 5, 2026Francisco M. Calatrava-Nicolás, Shoko Miyauchi, Vitor Fortes Rey +3Human Activity RecognitionDomain-Adversarial Learning

    Centre for Applied Autonomous Sensor Systems (AASS), Örebro University, Örebro, Sweden · Faculty of Information Science & Electrical Engineering, Kyushu University, Fukuoka, Japan · RPTU, Kaiserslautern, Germany +2