  We present MargNet, a deep learning-based classifier for identifying stars,
quasars and compact galaxies using photometric parameters and images from the
Sloan Digital Sky Survey (SDSS) Data Release 16 (DR16) catalogue. MargNet
consists of a combination of Convolutional Neural Network (CNN) and Artificial
Neural Network (ANN) architectures. Using a carefully curated dataset
consisting of 240,000 compact objects and an additional 150,000 faint objects,
the machine learns classification directly from the data, minimising the need
for human intervention. MargNet is the first classifier focusing exclusively on
compact galaxies and performs better than other methods to classify compact
galaxies from stars and quasars, even at fainter magnitudes. This model and
feature engineering in such deep learning architectures will provide greater
success in identifying objects in the ongoing and upcoming surveys, such as
Dark Energy Survey (DES) and images from the Vera C. Rubin Observatory.
