Variational Compression

Variational compression leverages probabilistic models, primarily variational autoencoders (VAEs), to achieve efficient data compression by learning latent representations of data. Current research focuses on improving compression ratios and speed through novel architectures like implicit neural representations (INRs) and hierarchical or split approaches, as well as optimizing for specific data modalities (e.g., speech, images) and resource-constrained environments (e.g., mobile edge computing). These advancements are significant for reducing storage needs and bandwidth requirements across diverse applications, impacting fields ranging from multimedia processing to machine learning.