  Detecting stellar clusters have always been an important research problem in
Astronomy. Although images do not convey very detailed information in detecting
stellar density enhancements, we attempt to understand if new machine learning
techniques can reveal patterns that would assist in drawing better inferences
from the available image data. This paper describes an unsupervised approach in
detecting star clusters using Deep Variational Autoencoder combined with a
Gaussian Mixture Model. We show that our method works significantly well in
comparison with state-of-the-art detection algorithm in recognizing a variety
of star clusters even in the presence of noise and distortion.
