Towards Understanding Normalization in Neural ODEs

Julia Gusak, Larisa Markeeva, Talgat Daulbaev, Alexander Katrutsa, Andrzej Cichocki, Ivan Oseledets

Keywords: Neural Ordinary Differential Equations, Normalization, Image Classification, Deep Learning

TL;DR: We investigate the effect of applying different normalization techniques in Neural ODEs for the image classification task.

Abstract: Normalization is an important and vastly investigated technique in deep learning. However, its role for Ordinary Differential Equation based networks (Neural ODEs) is still poorly understood. This paper investigates how different normalization techniques affect the performance of Neural ODEs. Particularly, we show that it is possible to achieve $93\%$ accuracy on the CIFAR-10 classification task, and to the best of our knowledge, this is the highest reported accuracy among Neural ODEs tested on this problem.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2004.09222/code)