%0 Personal Communication
%A Cao, Zhuo
%A Krieger, Lena
%A Scharr, Hanno
%A Assent, Ira
%T Galaxy Morphology Classification with Counterfactual Explanation
%M FZJ-2024-06463
%D 2024
%X Galaxy morphologies play an essential role in the study of the evolution of galaxies. The determination of morphologies is laborious for a large amount of data giving rise to machine learning-based approaches. Unfortunately, most of these approaches offer no insight into how the model works and make the results difficult to understand and explain. We here propose to extend a classical encoder-decoder architecture with invertible flow, allowing us to not only obtain a good predictive performance but also provide additional information about the decision process with counterfactual explanations.
%F PUB:(DE-HGF)20
%9 Minutes
%R 10.34734/FZJ-2024-06463
%U https://juser.fz-juelich.de/record/1033583