Feature Compression

Feature compression aims to reduce the size of data representations, particularly deep features from neural networks, while preserving essential information for downstream tasks. Current research focuses on developing efficient compression techniques, including those based on diffusion models, autoencoders, and attention mechanisms, often tailored to specific applications like image compression, video coding for machines (VCM), and federated learning. These advancements are crucial for improving the efficiency and scalability of machine learning systems, enabling deployment on resource-constrained devices and facilitating privacy-preserving collaborative learning.