Toward Open-World Domain Adaptation via Iteratively Contrastive Learning and Clustering

The open-set domain adaptation (DA) aims to address both covariate shift and category shift between a labeled source domain and an unlabeled target domain. Nevertheless, existing open-set DA methods always ignore the demand for discovering novel classes that are not present in the source domain and simply reject them as "unknown" sets without further exploration, which motivates us to understand the unknown sets more specifically. In this article, we present a more challenging open-world DA problem that recognizes seen classes while discovering novel classes in the target domain. To address this problem, we propose a novel framework that converts this problem into a clustering task via contrastive learning to learn pairwise relationships among the instances. More specifically, our method consists of two iterative steps. The semi-supervised clustering step clusters the unlabeled target data and separates it into seen and novel classes. In the contrastive learning step, based on the cluster assignments, we design tailored contrastive losses that learn pairwise relationships to reduce domain discrepancy and discover novel classes. Our method can be optimized as an example of expectation maximization (EM). We establish several baselines by extending related work. Our method obtains the superior performance on five public datasets, benchmarking this challenging setting for future research.