Towards the Generalization of Contrastive Self-Supervised Learning

Understand

Recently, self-supervised learning has attracted great attention, since it only requires unlabeled data for model training.

• Contrastive learning is one popular method for self-supervised learning and has achieved promising empirical performance.
• However, the theoretical understanding of its generalization ability is still limited.
• To this end, we define a kind of $(\sigma,\delta)$-measure to mathematically quantify the data augmentation, and then provide an upper bound of the downstream classification error rate based on the measure.