most citedDeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection

2 citations · 2 across the 1 of their papers we have counts for

collaborators

1 paper

astro-ph.GA2023★ 2 cited

DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection

A. Ćiprijanović, A. Lewis, K. Pedro +4

Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to t…