  The task of morphological classification is complex for simple
parameterization, but important for research in the galaxy evolution field.
Future galaxy surveys (e.g. EUCLID) will collect data about more than a $10^9$
galaxies. To obtain morphological information one needs to involve people to
mark up galaxy images, which requires either a considerable amount of money or
a huge number of volunteers. We propose an effective semi-supervised approach
for galaxy morphology classification task, based on active learning of
adversarial autoencoder (AAE) model. For a binary classification problem (top
level question of Galaxy Zoo 2 decision tree) we achieved accuracy 93.1% on the
test part with only 0.86 millions markup actions, this model can easily scale
up on any number of images. Our best model with additional markup achieves
accuracy of 95.5%. To the best of our knowledge it is a first time AAE
semi-supervised learning model used in astronomy.
