Contrastive Learning Framework

Contrastive learning frameworks aim to learn robust and informative representations by comparing similar (positive) and dissimilar (negative) data points. Current research focuses on applying this technique across diverse modalities, including text, images, graphs, and multimodal data, often employing variations of contrastive loss functions within neural network architectures. These frameworks are proving valuable for improving performance in various downstream tasks, such as classification, regression, clustering, and recommendation systems, particularly in scenarios with limited labeled data or complex data structures. The resulting advancements have significant implications for various fields, including natural language processing, computer vision, and recommendation systems.