Morphological classification of astronomical images with limited labelling
This addresses the challenge of efficiently classifying billions of galaxies for astronomical research, offering a scalable solution to reduce reliance on expensive or volunteer-based labeling.
The paper tackles the problem of morphological classification of galaxies with limited labeled data by proposing a semi-supervised approach based on active learning of an adversarial autoencoder, achieving 93.1% accuracy with only 0.86 million markup actions and up to 95.5% with additional markup.
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.