Redundancy Reduction

Redundancy reduction focuses on efficiently removing unnecessary or repetitive information from data, improving model performance, compression rates, and computational efficiency. Current research emphasizes developing algorithms and model architectures (e.g., transformers, neural networks) that identify and eliminate redundancy in various data types, including images, videos, speech, and graph data, often leveraging techniques like low-rank approximation, feature selection, and contrastive learning. These advancements are significant for improving the scalability and performance of machine learning models, enabling more efficient data storage and transmission, and enhancing the interpretability of learned representations across diverse scientific and engineering domains.