ORCID: https://orcid.org/0000-0001-9738-2487
(2022):
Cartoon Explanations of Image Classifiers.
17th European Conference on Computer Vision (ECCV 2022), Tel Aviv, Israel, October 23–27, 2022.
Avidan, Shai; Brostow, Gabriel; Cissé, Moustapha; Farinella, Giovanni Maria und Hassner, Tal (Hrsg.):
In: Computer Vision – ECCV 2022, Lecture Notes in Computer Science
Bd. 13672
Cham: Springer. S. 443-458
Abstract
We present CartoonX (Cartoon Explanation), a novel model-agnostic explanation method tailored towards image classifiers and based on the rate-distortion explanation (RDE) framework. Natural images are roughly piece-wise smooth signals—also called cartoon-like images—and tend to be sparse in the wavelet domain. CartoonX is the first explanation method to exploit this by requiring its explanations to be sparse in the wavelet domain, thus extracting the relevant piece-wise smooth part of an image instead of relevant pixel-sparse regions. We demonstrate that CartoonX can reveal novel valuable explanatory information, particularly for misclassifications. Moreover, we show that CartoonX achieves a lower distortion with fewer coefficients than state-of-the-art methods.
Dokumententyp: | Konferenzbeitrag (Paper) |
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Fakultät: | Mathematik, Informatik und Statistik > Mathematik
Mathematik, Informatik und Statistik > Mathematik > Professur für Mathematische Grundlagen des Verständnisses der künstlichen Intelligenz |
Themengebiete: | 500 Naturwissenschaften und Mathematik > 510 Mathematik |
ISSN: | 0302-9743 |
Ort: | Cham |
Sprache: | Englisch |
Dokumenten ID: | 110213 |
Datum der Veröffentlichung auf Open Access LMU: | 16. Apr. 2024 11:33 |
Letzte Änderungen: | 20. Mai 2025 11:09 |