Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection. (arXiv:2211.00677v3 [astro-ph.GA] UPDATED)

In the era of big astronomical surveys, our ability to leverage artificial
intelligence algorithms simultaneously for multiple datasets will open new
avenues for scientific discovery. Unfortunately, simply training a deep neural
network on images from one data domain often leads to very poor performance on
any other dataset. Here we develop a Universal Domain Adaptation method
DeepAstroUDA, capable of performing semi-supervised domain alignment that can
be applied to datasets with different types of class overlap. Extra classes can
be present …

anomaly anomaly detection arxiv astro classification detection domain adaptation semi-supervised survey