Unlocking the Potential of Disentangled Representation for Robust Media Understanding

Abstract

It has been argued that for AI to fundamentally understand the world around us, it must learn to identify and disentangle the underlying explanatory factors hidden in the observed environment of low-level sensory data. In this talk, I will first provide an overview of the recent developments in disentangled representation learning and identify some major trends. I will then present some applications of this powerful concept for robust media processing and understanding in tasks such as image restoration, super-resolution, classification, person re-ID, depth estimation, etc. I will also discuss some future directions.