Introduction

B-cos Nets on GitHub | B-cos Nets on YouTube (Computer Vision Seminar, MIT)

Today’s deep neural network architectures achieve impressive performances on a wide array of different tasks. In order to pull off these feats, they are trained ‘end-to-end’ on large amounts of data, which allow them to learn the most useful features for a given task on their own instead of relying on hand-crafted features designed by domain experts. While the possibility for end-to-end training has significantly boosted performance, this approach comes at a cost: as the feature extraction process is highly unconstrained, it is difficult to follow the decision making process throughout the network and thus hard to understand in hindsight how a deep neural network arrived at a particular decision.

With our work on B-cos Networks, we show that it is possible to train neural networks end-to-end whilst constraining how they are supposed to solve a task. By knowing the how, we can faithfully reflect the decision making process and produce highly detailed explanations for the model predictions. Despite these constraints, we show that B-cos Nets constitute performant classifiers.

Feel free to contact us if you have questions or comments: Author contact page

Copyright and license

Copyright (c) 2021 Moritz Böhle, Max-Planck-Gesellschaft

This code is licensed under the BSD License 2.0, see license.

Further, if you use any of the code in this repository for your research, please cite as:

  @inproceedings{Boehle2022CVPR,
          author    = {Moritz Böhle and Mario Fritz and Bernt Schiele},
          title     = {B-cos Networks: Alignment is All we Need for Interpretability},
          journal   = {IEEE/CVF Conference on Computer Vision and Pattern Recognition ({CVPR})},
          year      = {2022}
      }