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Convolutional Dynamic Alignment Networks for Interpretable Classifications
Official implementation of the CVPR 2021 paper (oral): Arxiv Paper | GitHub Pages
M. Böhle, M. Fritz, B. Schiele. Convolutional Dynamic Alignment Networks for Interpretable Classifications. CVPR, 2021.
<div style="displaystyle=block;align=center;"><p align="center" > <img src="docs/media/example_figure.png"/> </p> </div>Overview
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- Apart from the examples shown above, we additionally present some comparisons to post-hoc explanations evaluated on a CoDA-Net.
- In order to highlight the stability of the explanations, we additionally present contribution maps evaluated on videos here.
- For the latter, see interpretability/eval_on_videos.py, for the others check out the jupyter notebook CoDA-Networks Examples.
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- Here we present the quantitative results of the CoDA-Net interpretability for a model trained on CIFAR10.
- In order to reproduce these plots, check out the jupyter notebook CoDA-Networks Examples.
- For more information, also check out the code in interpretability/.
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Training
- In order to retrain the models according to the specifications outlined in the paper, see the experiments folder
Comparison to post-hoc explanation methods evaluated on the CoDA-Nets
<div style="displaystyle=block;align=center;"><p align="center" > <img src="docs/media/comparisons.png"/> </p> </div>Evaluated on videos
In order to highlight the stability of the contribution-based explanations of the CoDA-Nets, we present some examples for which the output for the respective class of the CoDA-Net was linearly decomposed frame by frame; for more information, see interpretability/eval_on_videos.py.
<div style="displaystyle=block;align=center"><p align="center"> <img width="240px" height="auto" src="docs/media/lorikeet.gif?raw=true"/> <img width="240px" height="auto" src="docs/media/drake2_atts.gif?raw=true"/> <img width="240px" height="auto" src="docs/media/birds_atts.gif?raw=true"/> <img width="240px" height="auto" src="docs/media/goldfinch.gif?raw=true"/> </p></div>Quantitative Interpretability results
In order to reproduce these plots, check out the jupyter notebook CoDA-Networks Examples. For more information, see the paper and check out the code at interpretability/
| <p align="center">Localisation metric</p> | <p align="center">Pixel removal metric</p> | |
|---|---|---|
| <p align="center">Compared to others</p> |  |  |
| <p align="center">Trained w/ different temperatures</p> |  |  |
Copyright and license
Copyright (c) 2021 Moritz Böhle, Max-Planck-Gesellschaft
This code is licensed under the BSD License 2.0, see license.
Further, you use any of the code in this repository for your research, please cite as:
@inproceedings{Boehle2021CVPR,
author = {Moritz Böhle and Mario Fritz and Bernt Schiele},
title = {Convolutional Dynamic Alignment Networks for Interpretable Classifications},
journal = {IEEE/CVF Conference on Computer Vision and Pattern Recognition ({CVPR})},
year = {2021}
}