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. (2021a). 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
Böhle, M. D., Fritz, M., & Schiele, B. (2021b). Optimising for Interpretability: Convolutional Dynamic Alignment Networks. Retrieved from https://arxiv.org/abs/2109.13004
(arXiv: 2109.13004) Abstract
We introduce a new family of neural network models called Convolutional<br>Dynamic Alignment Networks (CoDA Nets), which are performant classifiers with a<br>high degree of inherent interpretability. Their core building blocks are<br>Dynamic Alignment Units (DAUs), which are optimised to transform their inputs<br>with dynamically computed weight vectors that align with task-relevant<br>patterns. As a result, CoDA Nets model the classification prediction through a<br>series of input-dependent linear transformations, allowing for linear<br>decomposition of the output into individual input contributions. Given the<br>alignment of the DAUs, the resulting contribution maps align with<br>discriminative input patterns. These model-inherent decompositions are of high<br>visual quality and outperform existing attribution methods under quantitative<br>metrics. Further, CoDA Nets constitute performant classifiers, achieving on par<br>results to ResNet and VGG models on e.g. CIFAR-10 and TinyImagenet. Lastly,<br>CoDA Nets can be combined with conventional neural network models to yield<br>powerful classifiers that more easily scale to complex datasets such as<br>Imagenet whilst exhibiting an increased interpretable depth, i.e., the output<br>can be explained well in terms of contributions from intermediate layers within<br>the network.<br>
Export
BibTeX
@online{Boehle2109.13004,
TITLE = {Optimising for Interpretability: Convolutional Dynamic Alignment Networks},
AUTHOR = {B{\"o}hle, Moritz Daniel and Fritz, Mario and Schiele, Bernt},
LANGUAGE = {eng},
URL = {https://arxiv.org/abs/2109.13004},
EPRINT = {2109.13004},
EPRINTTYPE = {arXiv},
YEAR = {2021},
MARGINALMARK = {$\bullet$},
ABSTRACT = {We introduce a new family of neural network models called Convolutional<br>Dynamic Alignment Networks (CoDA Nets), which are performant classifiers with a<br>high degree of inherent interpretability. Their core building blocks are<br>Dynamic Alignment Units (DAUs), which are optimised to transform their inputs<br>with dynamically computed weight vectors that align with task-relevant<br>patterns. As a result, CoDA Nets model the classification prediction through a<br>series of input-dependent linear transformations, allowing for linear<br>decomposition of the output into individual input contributions. Given the<br>alignment of the DAUs, the resulting contribution maps align with<br>discriminative input patterns. These model-inherent decompositions are of high<br>visual quality and outperform existing attribution methods under quantitative<br>metrics. Further, CoDA Nets constitute performant classifiers, achieving on par<br>results to ResNet and VGG models on e.g. CIFAR-10 and TinyImagenet. Lastly,<br>CoDA Nets can be combined with conventional neural network models to yield<br>powerful classifiers that more easily scale to complex datasets such as<br>Imagenet whilst exhibiting an increased interpretable depth, i.e., the output<br>can be explained well in terms of contributions from intermediate layers within<br>the network.<br>},
}
Endnote
%0 Report
%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
%U https://arxiv.org/abs/2109.13004
%D 2021
%X We introduce a new family of neural network models called Convolutional<br>Dynamic Alignment Networks (CoDA Nets), which are performant classifiers with a<br>high degree of inherent interpretability. Their core building blocks are<br>Dynamic Alignment Units (DAUs), which are optimised to transform their inputs<br>with dynamically computed weight vectors that align with task-relevant<br>patterns. As a result, CoDA Nets model the classification prediction through a<br>series of input-dependent linear transformations, allowing for linear<br>decomposition of the output into individual input contributions. Given the<br>alignment of the DAUs, the resulting contribution maps align with<br>discriminative input patterns. These model-inherent decompositions are of high<br>visual quality and outperform existing attribution methods under quantitative<br>metrics. Further, CoDA Nets constitute performant classifiers, achieving on par<br>results to ResNet and VGG models on e.g. CIFAR-10 and TinyImagenet. Lastly,<br>CoDA Nets can be combined with conventional neural network models to yield<br>powerful classifiers that more easily scale to complex datasets such as<br>Imagenet whilst exhibiting an increased interpretable depth, i.e., the output<br>can be explained well in terms of contributions from intermediate layers within<br>the network.<br>
%K Statistics, Machine Learning, stat.ML,Computer Science, Computer Vision and Pattern Recognition, cs.CV,Computer Science, Learning, cs.LG