Metric-guided class-level alignment for domain adaptation

Abstract

The utilisation of domain adaptation methods facilitates the resolution of classification challenges in an unlabelled target domain by capitalising on the labelled information from source domains. Unfortunately, previous domain adaptation methods have focused mostly on global domain adaptation and have not taken into account class-specific data, which leads to poor knowledge transfer performance. The study of class-level domain adaptation, which aims to precisely match the distributions of different domains, has garnered attention in recent times. However, existing investigations into class-level alignment frequently align domain features either directly on or in close proximity to classification boundaries, resulting in the creation of uncertain samples that could potentially impair classification accuracy. To address the aforementioned problem, we propose a new approach called metric-guided class-level alignment (MCA) as a solution to this problem. Specifically, we employ different metrics to enable the network to acquire supplementary information, thereby enhancing class-level alignment. Moreover, MCA can be effectively combined with existing domain-level alignment methods to successfully mitigate the challenges posed by domain shift. Extensive testing on commonly-used public datasets shows that our method outperforms many other cutting-edge domain adaptation methods, showing significant gains over baseline performance.