About contrastive unsupervised representation learning for classification and its convergence

Understand

Contrastive representation learning has been recently proved to be very efficient for self-supervised training.

• These methods have been successfully used to train encoders which perform comparably to supervised training on downstream classification tasks.
• A few works have started to build a theoretical framework around contrastive learning in which guarantees for its performance can be proven.
• We provide extensions of these results to training with multiple negative samples and for multiway classification.