Why Is Everyone Training Very Deep Neural Network With Skip Connections?

Recent deep neural networks (DNNs) with several layers of feature representations rely on some form of skip connections to simultaneously circumnavigate optimization problems and improve generalization performance. However, the operations of these models are still not clearly understood, especially in comparison to DNNs without skip connections referred to as plain networks (PlainNets) that are absolutely untrainable beyond some depth. As such, the exposition of this article is the theoretical analysis of the role of skip connections in training very DNNs using concepts from linear algebra and random matrix theory.

Real-Time Enhancement of Dynamic Depth Videos with Non-Rigid Deformations.

We propose a novel approach for enhancing depth videos containing non-rigidly deforming objects. Depth sensors are capable of capturing depth maps in real-time but suffer from high noise levels and low spatial resolutions. While solutions for reconstructing 3D details in static scenes, or scenes with rigid global motions have been recently proposed, handling unconstrained non-rigid deformations in relative complex scenes remains a challenge.