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

• URL: http://arxiv.org/abs/2309.03531v2
• Date: Fri, 8 Sep 2023 07:43:50 GMT
• Title: A Robust Negative Learning Approach to Partial Domain Adaptation Using Source Prototypes
• Authors: Sandipan Choudhuri, Suli Adeniye, Arunabha Sen
• Abstract summary: This work proposes a robust Partial Domain Adaptation (PDA) framework that mitigates the negative transfer problem. It includes diverse, complementary label feedback, alleviating the effect of incorrect feedback and promoting pseudo-label refinement. We conducted a series of comprehensive experiments, including an ablation analysis, covering a range of partial domain adaptation tasks.
• Score: 0.8895157045883034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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