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@MISC{Cao:1033583,
      author       = {Cao, Zhuo and Krieger, Lena and Scharr, Hanno and Assent,
                      Ira},
      title        = {{G}alaxy {M}orphology {C}lassification with
                      {C}ounterfactual {E}xplanation},
      reportid     = {FZJ-2024-06463},
      year         = {2024},
      abstract     = {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.},
      cin          = {IAS-8},
      cid          = {I:(DE-Juel1)IAS-8-20210421},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5112},
      typ          = {PUB:(DE-HGF)20},
      doi          = {10.34734/FZJ-2024-06463},
      url          = {https://juser.fz-juelich.de/record/1033583},
}