Self-Labeling Framework for Open-Set Domain Adaptation With Few Labeled Samples

Abstract

Unsupervised domain adaptation (UDA) is extremely effective for transferring knowledge from a label-rich source domain to a label-scarce target domain. Because the target domain is unlabeled and may contain additional novel classes, open-set domain adaptation (ODA) has been suggested as a possible solution to detect these novel classes in the training phase. However, existing ODA methods rely heavily on abundant fully labeled source data, which are expensive to collect in specific applications and may also contain novel classes. In this study, we propose a novel self-labeling framework with prototypical contrastive learning and mutual information maximization to achieve ODA even when the amount of labeled data is very small, which is a new problem setting named few-shot ODA (FODA). We use self-supervised prototypical contrastive learning to train the network to learn the representations of source and target samples and maximize the mutual information between labels and input data to simultaneously recognize known and novel classes in the source and target domains. We evaluated our strategy in several domain adaptation environments and found that our method performed far better than existing approaches.