Multi View Contrastive Learning

Multi-view contrastive learning is a self-supervised learning technique that aims to learn robust and generalizable representations from data by comparing multiple augmented views of the same input. Current research focuses on enhancing the diversity and reliability of positive and negative sample pairs within contrastive learning frameworks, often employing graph neural networks or transformers, and incorporating strategies like data augmentation and view selection to improve representation learning. This approach has shown significant promise across diverse applications, including speech emotion recognition, medical image analysis, and knowledge graph reasoning, by improving model performance in scenarios with limited labeled data or significant domain shifts.