Domain Adaptation via Minimax Entropy for Real/Bogus Classification of
  Astronomical Alerts
Guillermo Cabrera-Vives César Bolivar Francisco Förster Alejandra M. Muñoz Arancibia Manuel Pérez-Carrasco Esteban Reyes
Time domain astronomy is advancing towards the analysis of multiple massive
datasets in real time, prompting the development of multi-stream machine
learning models. In this work, we study Domain Adaptation (DA) for real/bogus
classification of astronomical alerts using four different datasets: HiTS, DES,
ATLAS, and ZTF. We study the domain shift between these datasets, and improve a
naive deep learning classification model by using a fine tuning approach and
semi-supervised deep DA via Minimax Entropy (MME). We compare the balanced
accuracy of these models for different source-target scenarios. We find that
both the fine tuning and MME models improve significantly the base model with
as few as one labeled item per class coming from the target dataset, but that
the MME does not compromise its performance on the source dataset.