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Syst."],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to a related unlabeled target domain. Most existing works focus on minimizing the domain discrepancy to learn global domain-invariant representation using CNN-based architecture while ignoring both transferable and discriminative local representation, e.g, pixel-level and patch-level representation. In this paper, we propose the Transferable Adversarial Masked Self-distillation based on Vision Transformer architecture to enhance the transferability of UDA, named TAMS. Specifically, TAMS jointly optimizes three objectives to learn both task-specific class-level global representation and domain-specific local representation. First, we introduce adversarial masked self-distillation objective to distill representation from a full image to the representation predicted from a masked image, which aims to learn task-specific global class-level representation. Second, we introduce masked image modeling objectives to learn local pixel-level representation. Third, we introduce an adversarial weighted cross-domain adaptation objective to capture discriminative potentials of patch tokens, which aims to learn both transferable and discriminative domain-specific patch-level representation. Extensive studies on four benchmarks and the experimental results show that our proposed method can achieve remarkable improvements compared to previous state-of-the-art UDA methods.<\/jats:p>","DOI":"10.1007\/s40747-023-01094-4","type":"journal-article","created":{"date-parts":[[2023,5,24]],"date-time":"2023-05-24T05:01:51Z","timestamp":1684904511000},"page":"6567-6580","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Transferable adversarial masked self-distillation for unsupervised domain adaptation"],"prefix":"10.1007","volume":"9","author":[{"given":"Yuelong","family":"Xia","sequence":"first","affiliation":[]},{"given":"Li-Jun","family":"Yun","sequence":"additional","affiliation":[]},{"given":"Chengfu","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,24]]},"reference":[{"key":"1094_CR1","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. 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