Compressed Representation

Compressed representation research aims to reduce the size of data while preserving essential information for both human and machine processing. Current efforts focus on developing efficient compression algorithms and neural network architectures, such as autoencoders and latent diffusion models, to create compact representations for various data types, including images, videos, and 3D models, often leveraging techniques like contrastive learning and information bottleneck principles. This field is significant because efficient compressed representations are crucial for reducing storage needs, improving computational speed, and enabling the deployment of complex models on resource-constrained devices, impacting diverse applications from multimedia processing to brain data analysis.