Mini-Batch Optimization of Contrastive Loss

Understand

Contrastive learning has gained significant attention as a method for self-supervised learning.

• The contrastive loss function ensures that embeddings of positive sample pairs (e.g., different samples from the same class or different views of the same object) are similar, while embeddings of negative pairs are dissimilar.
• Practical constraints such as large memory requirements make it challenging to consider all possible positive and negative pairs, leading to the use of mini-batch optimization.
• In this paper, we investigate the theoretical aspects of mini-batch optimization in contrastive learning.