Uncovering the Intuition behind Capsule Networks and Inverse Graphics: Part I

'Capsule Networks' and'Inverse Graphics' seem like intimidating and somewhat vague terms when heard for the first time. These terms weren't prevalent in mainstream media until recently, after the godfather of deep learning, Geoffrey Hinton, came out with two papers on Dynamic Routing between Capsules and on Matrix Capsules with EM Routing [This is currently a blind submission under review for ICLR 2018 but let's be honest, we know it's going to be Hinton et al.]. In this article, I will try to distill these ideas and explain the intuition behind them and how these are bringing machine learning models in computer vision one step closer to emulating human vision. Starting with the intuition behind CNNs, I'll dive into how they arise from our hypotheses about the neuroscience behind human sight and how inverse graphics is the way to create the next generation of computer vision systems and finally give a brief overview of how all of this connects to Capsule Networks. Research about the neuroscience and human sight led us to realize the fact that humans learn and analyze visual information hierarchically.