Zoom Link: https://stanford.zoom.us/j/91328398114?pwd=UlE1YnVNQU90RGpYcTFKamg4YTIzdz09

Abstract: Deep Learning-based Data Compression using non-linear transform coding has recently attracted significant attention due to its ability to achieve very attractive rate-distortion (RD) curves. Unfortunately, many existing methods rely on large and expressive architectures with high decoding complexity, rendering current techniques impractical.

In this talk, we instead introduce a new way of approaching this problem through a functional view of data using Neural Fields, also known as Implicit Neural Representations or NeRFs. We first introduce the general concept of Neural Fields as alternatives to multi-dimensional arrays for data representation and provide a quick overview of foundational work in the field. We then proceed to cover a series of approaches designed to leverage this perspective for Neural Data Compression on a wide range of modalities, touching on architectural improvements, spatial representations, sparse neural networks and meta-learning. Finally, we conclude by introducing C3, our last neural compression method designed for images and videos, showing how we can match the RD performance of Video Compression Transformers on the UVG video benchmark while using less than 0.1% of their MACs/pixel for decoding, thus showing a promising potential avenue towards overcoming one of the open major problems for Neural Compression.