Self-supervised learning with automatic data augmentation for enhancing representation

Self-supervised learning has become an increasingly popular method for learning effective representations from unlabeled data. One prominent approach in self-supervised learning is contrastive learning, which trains models to distinguish between similar and dissimilar sample pairs by pulling similar pairs closer and pushing dissimilar pairs farther apart. The key to the success of contrastive learning lies in the quality of the data augmentation, which increases the diversity of the data and helps the model learn more powerful and generalizable representations. While many studies have emphasized the importance of data augmentation, however, most of them rely on human-crafted augmentation strategies. In this paper, we propose a novel method, Self Augmentation on Contrastive Learning with Clustering (SACL), searching for the optimal data augmentation policy automatically using Bayesian optimization and clustering. The proposed approach overcomes the limitations of relying on domain knowledge and avoids the high costs associated with manually designing data augmentation rules. It automatically captures informative and useful features within the data by exploring augmentation policies. We demonstrate that the proposed method surpasses existing approaches that rely on manually designed augmentation rules. Our experiments show SACL outperforms manual strategies, achieving a performance improvement of 1.68% and 1.57% over MoCo v2 on the CIFAR10 and SVHN datasets, respectively.