TY  - PCOMM
AU  - Cao, Zhuo
AU  - Krieger, Lena
AU  - Scharr, Hanno
AU  - Assent, Ira
TI  - Galaxy Morphology Classification with Counterfactual Explanation
M1  - FZJ-2024-06463
PY  - 2024
AB  - 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.
LB  - PUB:(DE-HGF)20
DO  - DOI:10.34734/FZJ-2024-06463
UR  - https://juser.fz-juelich.de/record/1033583
ER  -