Moritz Böhle
I am a PhD student in the Computer Vision and Machine Learning group at the Max Planck Institute for Informatics in Saarbrücken, Germany.
I am advised by Prof. Bernt Schiele and Prof. Mario Fritz. My research is focused on interpretable, trustworthy, and responsible deep learning.
In particular, over the course of my PhD, I explored how to design inherently interpretable deep neural networks such as the CoDA and the B-cos Networks,
which are optimised to provide inherent explanations that highlight important input features. Before commencing my doctoral studies, I obtained a Bachelor’s
degree in Physics at the Free University in Berlin, Germany, in 2016 and a Master’s degree at the Bernstein Center for Computational Neuroscience in Berlin, Germany, in 2019.
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B-cos Alignment for Inherently Interpretable CNNs and Vision Transformers
Moritz Böhle,
Navdeeppal Singh,
Mario Fritz,
Bernt Schiele
TPAMI, 2024
Paper | Code
| Talk @ MIT Vision & Graphics Seminar
| Talk @ XAI4CV Workshop
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Better Understanding Differences in Attribution Methods via Systematic Evaluations
Sukrut Rao,
Moritz Böhle,
Bernt Schiele
TPAMI, 2024
Paper | Code
| Video
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Studying How to Efficiently and Effectively Guide Models with Explanations.
Sukrut Rao*,
Moritz Böhle*,
Amin Parchami-Araghi,
Bernt Schiele
(*equal contribution)
ICCV, 2023
Paper
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| Video
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Temperature Schedules for Self-Supervised Contrastive Methods on Long-Tail Data
Anna Kukleva*,
Moritz Böhle*,
Bernt Schiele,
Hilde Kuehne,
Christian Rupprecht
(*equal contribution)
ICLR, 2023
Paper | Code
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B-cos Networks: Alignment is All We Need for Interpretability
Moritz Böhle,
Mario Fritz,
Bernt Schiele
CVPR, 2022
Paper
| Code
| Talk @ MIT Vision & Graphics Seminar
| Talk @ XAI4CV Workshop
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Towards Better Understanding Attribution Methods
Sukrut Rao,
Moritz Böhle,
Bernt Schiele
CVPR, 2022
Paper
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| Video
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Optimising for Interpretability: Convolutional Dynamic Alignment Networks
Moritz Böhle,
Mario Fritz,
Bernt Schiele
TPAMI, 2022
Paper | Code | Video
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Convolutional Dynamic Alignment Networks for Interpretable Classifications
Moritz Böhle,
Mario Fritz,
Bernt Schiele
CVPR (oral), 2021
Paper
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| Video
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Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification
Moritz Böhle*,
Fabian Eitel*,
Martin Weygandt,
Kerstin Ritter
(*equal contribution)
Frontiers in Aging Neuroscience, 2019
Paper | Code
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I am further proud to have served as a reviewer for IEEE PAMI, CVPR, ECCV, ICLR, NeurIPS, IEEE Trans. Inf. Forensics Secur., and ICML, and happy to have been named an outstanding reviewer for ECCV 2022!
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