guest ::
| Home > Publications database > Galaxy Morphology Classification with Counterfactual Explanation > print |
| Home > Publications database > Galaxy Morphology Classification with Counterfactual Explanation > print |
| 001 | 1033583 | ||
| 005 | 20251217202222.0 | ||
| 024 | 7 | _ | |a 10.34734/FZJ-2024-06463 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2024-06463 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Cao, Zhuo |0 P:(DE-Juel1)199019 |b 0 |u fzj |
| 245 | _ | _ | |a Galaxy Morphology Classification with Counterfactual Explanation |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |a Text |2 DataCite |
| 336 | 7 | _ | |a Minutes |b minutes |m minutes |0 PUB:(DE-HGF)20 |s 1765959395_26386 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a MISC |2 BibTeX |
| 336 | 7 | _ | |a OTHER |2 ORCID |
| 336 | 7 | _ | |a Other |2 DINI |
| 336 | 7 | _ | |a Personal Communication |0 4 |2 EndNote |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5112 |c POF4-511 |f POF IV |x 0 |
| 650 | 2 | 7 | |a Others |0 V:(DE-MLZ)SciArea-250 |2 V:(DE-HGF) |x 0 |
| 700 | 1 | _ | |a Krieger, Lena |0 P:(DE-Juel1)196726 |b 1 |u fzj |
| 700 | 1 | _ | |a Scharr, Hanno |0 P:(DE-Juel1)129394 |b 2 |u fzj |
| 700 | 1 | _ | |a Assent, Ira |0 P:(DE-Juel1)188313 |b 3 |u fzj |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1033583/files/Counterfactuals_Galaxy_JUSER.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:1033583 |p openaire |p open_access |p VDB |p driver |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)199019 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)196726 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)129394 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)188313 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5112 |x 0 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-8-20210421 |k IAS-8 |l Datenanalyse und Maschinenlernen |x 0 |
| 980 | 1 | _ | |a FullTexts |
| 980 | _ | _ | |a minutes |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-8-20210421 |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|