View Contrastive Learning

View contrastive learning aims to improve representation learning by comparing multiple "views" of the same data point, generated through augmentations or observed from different modalities. Current research focuses on extending this approach to multiple related views ("poly-view"), incorporating diverse data sources (e.g., sequence and structure information for peptides, multiple medical images), and employing novel loss functions like additive margin contrastive losses to enhance discriminative power. This technique shows promise in various applications, including medical image analysis, peptide encoding, and solving complex problems like mathematical word problems, by leveraging richer data representations and improving model generalization and efficiency.