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