Compact Latent

Compact latent representations aim to efficiently encode high-dimensional data into lower-dimensional spaces, preserving crucial information while minimizing redundancy. Current research focuses on developing novel architectures, such as variational autoencoders (VAEs) and self-attention networks, to achieve this compression, often within hyperbolic spaces or through information-theoretic approaches like the information bottleneck principle. This work is significant for improving the efficiency and performance of various machine learning tasks, including image reconstruction, multimodal learning, and autonomous driving, by enabling faster processing and better generalization.