IPNet: Interpretable Prototype Network for Multi-Source Domain Adaptation

Abstract

Multi-source domain adaptation (MSDA) borrows intrinsic knowledge from well-annotated source domains to identify target visual signals. The main challenges are effectively mitigating cross-domain shift and extracting discriminative target features via the suitable source semantics. To overcome them, this paper proposes a novel Interpretable Prototype Network (IPNet) with channel-wise augmentation and multi-domain prototype mechanism. Specifically, IPNet explores the parameterized channel fusion paradigm across multiple source domains and target one to generate intermediate instances and achieve beneficial alignment. Moreover, IPNet analyzes contributions of source domains with interpretable learning approach and adjusts their effects on representations of target signals. Extensive experiments on three MSDA benchmark datasets suggest the advantages of our IPNet over others and exhibit the path of knowledge transfer.