Self-supervised learning with automatic data augmentation for enhancing representation

Chanjong Park, Eunwoo Kim

Abstract: Highlights•Optimal augmentation for robust, discriminative representations in contrastive learning.•Diverse transformations for adaptable augmentation strategies across datasets.•Bayesian optimization to find effective augmentation policies with minimal computation.•Weighted combination of contrastive loss and clustering score for data-specific optimization.