Towards a Rigorous Analysis of Mutual Information in Contrastive Learning

Understand

Contrastive learning has emerged as a cornerstone in recent achievements of unsupervised representation learning.

• Its primary paradigm involves an instance discrimination task with a mutual information loss.
• The loss is known as InfoNCE and it has yielded vital insights into contrastive learning through the lens of mutual information analysis.
• However, the estimation of mutual information can prove challenging, creating a gap between the elegance of its mathematical foundation and the complexity of its estimation.