DASSL: Domain Agnostic Self-Supervised Learning with Multiple Missing Information Reconstruction Branches

Abstract

Self-supervised learning (SSL) is a technique used to learn feature representations from unlabeled data. However, existing SSL frameworks either rely too heavily on domain knowledge due to their design based on feature invariance, leading to a lack of domain transferability, or they are based on autoencoder designs, which generate features with redundant low-level semantics, resulting in suboptimal model representations. In this work, we introduce a novel Domain Agnostic Self-Supervised Learning framework called DASSL, which learns superior high-level feature representations of samples by reconstructing the samples’ missing information in the representation space. DASSL does not require additional domain priors, and compared to successful SSL methods, DASSL achieves competitive representation quality. Moreover, when DASSL incorporates domain-related data augmentation techniques, it outperforms successful methods across multiple datasets and evaluation protocols.