Neural Distributed Source Coding

Explore a groundbreaking framework for lossy Distributed Source Coding (DSC) in this 24-minute lecture by Hyeji Kim from The University of Texas at Austin. Delve into a novel approach that overcomes limitations of traditional DSC methods by utilizing a conditional Vector-Quantized Variational AutoEncoder (VQ-VAE) to learn distributed encoders and decoders. Discover how this technique achieves state-of-the-art PSNR while handling complex correlations and scaling to high dimensions, all without relying on hand-crafted source modeling. Gain insights into the application of this method across multiple datasets and understand its potential to revolutionize practical DSC implementation beyond synthetic datasets and specific correlation structures.

Neural Distributed Source Coding