Di Chen
Personal Information
Publications
Böhle, M., Fritz, M., & Schiele, B. (2022). B-cos Networks: Alignment is All We Need for Interpretability. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA: IEEE. doi:10.1109/CVPR52688.2022.01008
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BibTeX
@inproceedings{Boehle_CVPR2022,
TITLE = {B-cos Networks: {A}lignment is All We Need for Interpretability},
AUTHOR = {B{\"o}hle, Moritz and Fritz, Mario and Schiele, Bernt},
LANGUAGE = {eng},
ISBN = {978-1-6654-6946-3},
DOI = {10.1109/CVPR52688.2022.01008},
PUBLISHER = {IEEE},
YEAR = {2022},
MARGINALMARK = {$\bullet$},
BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022)},
PAGES = {10319--10328},
ADDRESS = {New Orleans, LA, USA},
}
Endnote
%0 Conference Proceedings
%A Böhle, Moritz
%A Fritz, Mario
%A Schiele, Bernt
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
%T B-cos Networks: Alignment is All We Need for Interpretability :
%G eng
%U http://hdl.handle.net/21.11116/0000-000A-6F96-1
%R 10.1109/CVPR52688.2022.01008
%D 2022
%B 35th IEEE/CVF Conference on Computer Vision and Pattern Recognition
%Z date of event: 2022-06-19 - 2022-06-24
%C New Orleans, LA, USA
%B IEEE/CVF Conference on Computer Vision and Pattern Recognition
%P 10319 - 10328
%I IEEE
%@ 978-1-6654-6946-3
Rao, S., Böhle, M., & Schiele, B. (2022). Towards Better Understanding Attribution Methods. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022). New Orleans, LA, USA: IEEE. doi:10.1109/CVPR52688.2022.00998
Export
BibTeX
@inproceedings{Rao_CVPR2022,
TITLE = {Towards Better Understanding Attribution Methods},
AUTHOR = {Rao, Sukrut and B{\"o}hle, Moritz and Schiele, Bernt},
LANGUAGE = {eng},
ISBN = {978-1-6654-6946-3},
DOI = {10.1109/CVPR52688.2022.00998},
PUBLISHER = {IEEE},
YEAR = {2022},
MARGINALMARK = {$\bullet$},
BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022)},
PAGES = {10213--10222},
ADDRESS = {New Orleans, LA, USA},
}
Endnote
%0 Conference Proceedings
%A Rao, Sukrut
%A Böhle, Moritz
%A Schiele, Bernt
%+ Computer Graphics, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
%T Towards Better Understanding Attribution Methods :
%G eng
%U http://hdl.handle.net/21.11116/0000-000A-6F91-6
%R 10.1109/CVPR52688.2022.00998
%D 2022
%B 35th IEEE/CVF Conference on Computer Vision and Pattern Recognition
%Z date of event: 2022-06-19 - 2022-06-24
%C New Orleans, LA, USA
%B IEEE/CVF Conference on Computer Vision and Pattern Recognition
%P 10213 - 10222
%I IEEE
%@ 978-1-6654-6946-3
Böhle, M. D., Fritz, M., & Schiele, B. (2022). Optimising for Interpretability: Convolutional Dynamic Alignment Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. doi:10.1109/TPAMI.2022.3226041
Export
BibTeX
@article{Boehle2109.13004,
TITLE = {Optimising for Interpretability: Convolutional Dynamic Alignment Networks},
AUTHOR = {B{\"o}hle, Moritz Daniel and Fritz, Mario and Schiele, Bernt},
LANGUAGE = {eng},
ISSN = {0162-8828},
DOI = {10.1109/TPAMI.2022.3226041},
PUBLISHER = {IEEE},
ADDRESS = {Piscataway, NJ},
YEAR = {2022},
MARGINALMARK = {$\bullet$},
JOURNAL = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
}
Endnote
%0 Journal Article
%A Böhle, Moritz Daniel
%A Fritz, Mario
%A Schiele, Bernt
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
%T Optimising for Interpretability: Convolutional Dynamic Alignment
Networks :
%G eng
%U http://hdl.handle.net/21.11116/0000-0009-8113-F
%R 10.1109/TPAMI.2022.3226041
%7 2022
%D 2022
%J IEEE Transactions on Pattern Analysis and Machine Intelligence
%O IEEE Trans. Pattern Anal. Mach. Intell.
%I IEEE
%C Piscataway, NJ
%@ false
Böhle, M. D., Fritz, M., & Schiele, B. (2021). Convolutional Dynamic Alignment Networks for Interpretable Classifications. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). Nashville, TN, USA (Virtual): IEEE. doi:10.1109/CVPR46437.2021.00990
Export
BibTeX
@inproceedings{Boehle_CVPR21,
TITLE = {Convolutional Dynamic Alignment Networks for Interpretable Classifications},
AUTHOR = {B{\"o}hle, Moritz Daniel and Fritz, Mario and Schiele, Bernt},
LANGUAGE = {eng},
ISBN = {978-1-6654-4509-2},
DOI = {10.1109/CVPR46437.2021.00990},
PUBLISHER = {IEEE},
YEAR = {2021},
MARGINALMARK = {$\bullet$},
BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021)},
PAGES = {10029--10038},
ADDRESS = {Nashville, TN, USA (Virtual)},
}
Endnote
%0 Conference Proceedings
%A Böhle, Moritz Daniel
%A Fritz, Mario
%A Schiele, Bernt
%+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
External Organizations
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society
%T Convolutional Dynamic Alignment Networks for Interpretable Classifications :
%G eng
%U http://hdl.handle.net/21.11116/0000-0008-1863-E
%R 10.1109/CVPR46437.2021.00990
%D 2021
%B 34th IEEE Conference on Computer Vision and Pattern Recognition
%Z date of event: 2021-06-19 - 2021-06-25
%C Nashville, TN, USA (Virtual)
%B IEEE/CVF Conference on Computer Vision and Pattern Recognition
%P 10029 - 10038
%I IEEE
%@ 978-1-6654-4509-2