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  Towards Designing Inherently Interpretable Deep Neural Networks for Image Classification

Böhle, M. (2024). Towards Designing Inherently Interpretable Deep Neural Networks for Image Classification. PhD Thesis, Universität des Saarlandes, Saarbrücken. doi:10.22028/D291-42190.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000F-76AA-D Version Permalink: https://hdl.handle.net/21.11116/0000-000F-76AD-A
Genre: Thesis

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 Creators:
Böhle, Moritz1, 2, Author           
Schiele, Bernt1, Advisor                 
Fritz, Mario3, Advisor           
Akata, Zeynep3, Referee                 
Brendel, Wieland3, Referee
Affiliations:
1Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society, ou_1116547              
2International Max Planck Research School, MPI for Informatics, Max Planck Society, Campus E1 4, 66123 Saarbrücken, DE, ou_1116551              
3External Organizations, ou_persistent22              

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Language(s): eng - English
 Dates: 2023-05-032024-06-212024
 Publication Status: Issued
 Pages: XIII, 265 p.
 Publishing info: Saarbrücken : Universität des Saarlandes
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: BoehlePhD24
URN: urn:nbn:de:bsz:291--ds-421904
DOI: 10.22028/D291-42190
Other: hdl:20.500.11880/37907
 Degree: PhD

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