  In general, an experimental environment for deep learning assumes that the
training and the test dataset are sampled from the same distribution. However,
in real-world situations, a difference in the distribution between two
datasets, domain shift, may occur, which becomes a major factor impeding the
generalization performance of the model. The research field to solve this
problem is called domain generalization, and it alleviates the domain shift
problem by extracting domain-invariant features explicitly or implicitly. In
recent studies, contrastive learning-based domain generalization approaches
have been proposed and achieved high performance. These approaches require
sampling of the negative data pair. However, the performance of contrastive
learning fundamentally depends on quality and quantity of negative data pairs.
To address this issue, we propose a new regularization method for domain
generalization based on contrastive learning, self-supervised contrastive
regularization (SelfReg). The proposed approach use only positive data pairs,
thus it resolves various problems caused by negative pair sampling. Moreover,
we propose a class-specific domain perturbation layer (CDPL), which makes it
possible to effectively apply mixup augmentation even when only positive data
pairs are used. The experimental results show that the techniques incorporated
by SelfReg contributed to the performance in a compatible manner. In the recent
benchmark, DomainBed, the proposed method shows comparable performance to the
conventional state-of-the-art alternatives. Codes are available at
https://github.com/dnap512/SelfReg.
