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This work proposes a robust Partial Domain Adaptation (PDA) framework thatmitigates the negative transfer problem by incorporating a robusttarget-supervision strategy. It leverages ensemble learning and includesdiverse, complementary label feedback, alleviating the effect of incorrectfeedback and promoting pseudo-label refinement. Rather than relying exclusivelyon first-order moments for distribution alignment, our approach offers explicitobjectives to optimize intra-class compactness and inter-class separation withthe inferred source prototypes and highly-confident target samples in adomain-invariant fashion. Notably, we ensure source data privacy by eliminatingthe need to access the source data during the adaptation phase through a prioriinference of source prototypes. We conducted a series of comprehensiveexperiments, including an ablation analysis, covering a range of partial domainadaptation tasks. Comprehensive evaluations on benchmark datasets corroborateour framework's enhanced robustness and generalization, demonstrating itssuperiority over existing state-of-the-art PDA approaches.

A Robust Negative Learning Approach to Partial Domain Adaptation Using Source Prototypes