Multi-source domain adaptation handling inaccurate label spaces

Abstract

Domain adaptation with inaccurate label is a challenging and interesting topic in transfer learning, dealing with source and target domains with shift label spaces. Most existing domain adaptation methods assume aware label distributions among source and target domains. However, this cannot always be guaranteed in reality. Furthermore, existing multi-domain adaptation methods rarely deal with label heterogeneity among source domains. Thus, in this paper, we propose a multi-source domain adaptation method handling Inaccurate label (IncLabDA) during transfer. The proposed method designs a module that can transfer knowledge from multi-source domains with both homogeneous and heterogeneous label spaces in universal scenario. Anchors are generated from pre-trained model to build data-matching via a contrastive method avoiding to referring original data. In addition, class center consistency combined with clustering strategy considering both global and local confidences is adopted to recognize out-of-distribution samples. By removing source private classes and target unknown samples, highly confident target samples are collected to self-supervise the adaptation. At the same time, constraints enlarging the distance among target known classes and between the known and unknown samples are applied to enhance the performance of the proposed model. Experiments on real-world datasets validate the superiority of the IncLabDA model.