Towards Designing Inherently Interpretable Deep Neural Networks for Image 
Classification

Böhle, Moritz

doi:10.22028/D291-42190

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Thesis

Towards Designing Inherently Interpretable Deep Neural Networks for Image Classification

MPS-Authors

/persons/resource/persons230363

Böhle, Moritz
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

External Resource

https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/37907
(Any fulltext)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

phd_thesis-moritz_boehle-sulb.pdf
(Any fulltext), 94MB

Supplementary Material (public)

There is no public supplementary material available

Citation

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

Cite as: https://hdl.handle.net/21.11116/0000-000F-76AA-D

Abstract

There is no abstract available