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- W3119341693 abstract "Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the closeness of cross-domain class centroids. However, the cross-domain inner-class compactness and the underlying fine-grained subtype structure remained largely underexplored. In this work, we propose to adaptively carry out the fine-grained subtype-aware alignment by explicitly enforcing the class-wise separation and subtype-wise compactness with intermediate pseudo labels. Our key insight is that the unlabeled subtypes of a class can be divergent to one another with different conditional and label shifts, while inheriting the local proximity within a subtype. The cases with or without the prior information on subtype numbers are investigated to discover the underlying subtype structure in an online fashion. The proposed subtype-aware dynamic UDA achieves promising results on a medical diagnosis task." @default.
- W3119341693 created "2021-01-18" @default.
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- W3119341693 date "2021-05-18" @default.
- W3119341693 modified "2023-09-27" @default.
- W3119341693 title "Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis" @default.
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- W3119341693 doi "https://doi.org/10.1609/aaai.v35i3.16317" @default.
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