  This work proposes a robust Partial Domain Adaptation (PDA) framework that
mitigates the negative transfer problem by incorporating a robust
target-supervision strategy. It leverages ensemble learning and includes
diverse, complementary label feedback, alleviating the effect of incorrect
feedback and promoting pseudo-label refinement. Rather than relying exclusively
on first-order moments for distribution alignment, our approach offers explicit
objectives to optimize intra-class compactness and inter-class separation with
the inferred source prototypes and highly-confident target samples in a
domain-invariant fashion. Notably, we ensure source data privacy by eliminating
the need to access the source data during the adaptation phase through a priori
inference of source prototypes. We conducted a series of comprehensive
experiments, including an ablation analysis, covering a range of partial domain
adaptation tasks. Comprehensive evaluations on benchmark datasets corroborate
our framework's enhanced robustness and generalization, demonstrating its
superiority over existing state-of-the-art PDA approaches.
