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  Convolutional Dynamic Alignment Networks for Interpretable Classifications

Böhle, M. D., Fritz, M., & Schiele, B. (in press). Convolutional Dynamic Alignment Networks for Interpretable Classifications. In IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0008-1863-E Version Permalink: https://hdl.handle.net/21.11116/0000-0008-1864-D
Genre: Conference Paper

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 Creators:
Böhle, Moritz Daniel1, Author           
Fritz, Mario2, Author           
Schiele, Bernt1, Author           
Affiliations:
1Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society, ou_1116547              
2External Organizations, ou_persistent22              

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Language(s): eng - English
 Dates: 2021
 Publication Status: Accepted / In Press
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Boehle_CVPR21
 Degree: -

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Title: 34th IEEE Conference on Computer Vision and Pattern Recognition
Place of Event: Virtual Conference
Start-/End Date: 2021-06-19 - 2021-06-25

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Title: IEEE/CVF Conference on Computer Vision and Pattern Recognition
  Abbreviation : CVPR 2021
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: Piscataway, NJ : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -