Galaxy Morphology Classification using Neural Ordinary Differential
  Equations
  We introduce a continuous depth version of the Residual Network (ResNet)
called Neural ordinary differential equations (NODE) for the purpose of galaxy
morphology classification. We carry out a classification of galaxy images from
the Galaxy Zoo 2 dataset, consisting of five distinct classes, and obtained an
accuracy between 91-95\%, depending on the image class. We train NODE with
different numerical techniques such as adjoint and Adaptive Checkpoint Adjoint
(ACA) and compare them against ResNet. While ResNet has certain drawbacks, such
as time consuming architecture selection (e.g. the number of layers) and the
requirement of a large dataset needed for training, NODE can overcome these
limitations. Through our results, we show that that the accuracy of NODE is
comparable to ResNet, and the number of parameters used is about one-third as
compared to ResNet, thus leading to a smaller memory footprint, which would
benefit next generation surveys.
