| Abstract : | 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. |
