  Self-supervised learning (SSL) has emerged as a promising solution for
addressing the challenge of limited labeled data in deep neural networks
(DNNs), offering scalability potential. However, the impact of design
dependencies within the SSL framework remains insufficiently investigated. In
this study, we comprehensively explore SSL behavior across a spectrum of
augmentations, revealing their crucial role in shaping SSL model performance
and learning mechanisms. Leveraging these insights, we propose a novel learning
approach that integrates prior knowledge, with the aim of curtailing the need
for extensive data augmentations and thereby amplifying the efficacy of learned
representations. Notably, our findings underscore that SSL models imbued with
prior knowledge exhibit reduced texture bias, diminished reliance on shortcuts
and augmentations, and improved robustness against both natural and adversarial
corruptions. These findings not only illuminate a new direction in SSL
research, but also pave the way for enhancing DNN performance while
concurrently alleviating the imperative for intensive data augmentation,
thereby enhancing scalability and real-world problem-solving capabilities.
