Residual Networks as Geodesic Flows of Diffeomorphisms

24 May 2018Francois RousseauRonan Fablet

This paper addresses the understanding and characterization of residual networks (ResNet), which are among the state-of-the-art deep learning architectures for a variety of supervised learning problems. We focus on the mapping component of ResNets, which map the embedding space towards a new unknown space where the prediction or classification can be stated according to linear criteria... (read more)

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