Contrastive Learning with Consistent Representations

Understand

Contrastive learning demonstrates great promise for representation learning.

• Data augmentations play a critical role in contrastive learning by providing informative views of the data without necessitating explicit labels.
• Nonetheless, the efficacy of current methodologies heavily hinges on the quality of employed data augmentation (DA) functions, often chosen manually from a limited set of options.
• While exploiting diverse data augmentations is appealing, the complexities inherent in both DAs and representation learning can lead to performance deterioration.