Improved triplet loss for domain adaptation

Abstract

A technique known as domain adaptation is utilised to address classification challenges in an unlabelled target domain by leveraging labelled source domains. Previous domain adaptation approaches have predominantly focussed on global domain adaptation, neglecting class-level information and resulting in suboptimal transfer performance. In recent years, a considerable number of researchers have explored class-level domain adaptation, aiming to precisely align the distribution of diverse domains. Nevertheless, existing research on class-level alignment tends to align domain features either on or in proximity to classification boundaries, which introduces ambiguous samples that can impact classification accuracy. In this study, the authors propose a novel strategy called class guided constraints (CGC) to tackle this issue. Specifically, CGC is employed to preserve the compactness within classes and separability between classes of domain features prior to class-level alignment. Furthermore, the authors incorporate CGC in conjunction with similarity guided constraint. Comprehensive evaluations conducted on four public datasets demonstrate that our approach outperforms numerous state-of-the-art domain adaptation methods significantly and achieves greater improvements compared to the baseline approach.