Overall Compression

Overall compression in various domains, from images and datasets to neural networks, aims to reduce data size while preserving essential information or functionality. Current research focuses on developing efficient algorithms and architectures, including learned quantization, hierarchical compression techniques, and the use of tensor networks, to achieve high compression ratios with minimal information loss. These advancements are crucial for improving the efficiency and scalability of machine learning, data storage, and communication systems, impacting both computational resource usage and the feasibility of deploying large models on resource-constrained devices. The development of standardized evaluation metrics is also a key area of focus to facilitate comparison and progress in the field.