Multi View Network

Multi-view networks leverage information from multiple data sources (views) to improve performance in various tasks, primarily aiming to overcome limitations of single-view approaches by exploiting complementary information and reducing noise. Current research focuses on developing novel architectures, such as transformer-based networks and those incorporating information bottleneck principles or prototype learning, to effectively fuse multi-view data and enhance model interpretability. These advancements have significant implications across diverse fields, including medical image analysis (e.g., improved diagnosis of neurodevelopmental disorders and cancer), multimedia retrieval, and recommender systems, by enabling more accurate, robust, and insightful analyses.