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Memory-Assisted Sub-Prototype Mining for Universal Domain Adaptation
To address these issues, we propose a novel Memory-Assisted Sub-Prototype Mining (MemSPM) method that can learn the differences between samples belonging to the same category and mine sub-classes when there exists significant concept shift between them.
COCA: Classifier-Oriented Calibration via Textual Prototype for Source-Free Universal Domain Adaptation
SF-UniDA methods eliminate the need for direct access to source samples when performing adaptation to the target domain.
Self-Paced Learning for Open-Set Domain Adaptation
Open-set domain adaptation aims to not only recognize target samples belonging to common classes shared by source and target domains but also perceive unknown class samples.
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