Relevance Regularization of Convolutional Neural Network for Interpretable Classification
Abstract
Conventional end-to-end learning algorithm considers only the final prediction output and ignores layer-wise relational reasoning during the training. In this paper, we propose to use a forward and backward interacted-activation (FBI) loss function that regularizes training a CNN so that the prediction model can provide interpretable results for classification. From our best knowledge, the proposed algorithm is the first work to use a regularization function without any prior knowledge or pre-defined terms to allow for a CNN to be more explainable. It is demonstrated with quantitative and qualitative analysis that the proposed technique can be used for efficiently train a CNN with more interpretability, applied to a well-known classification problem.
Cite
Text
Yoo et al. "Relevance Regularization of Convolutional Neural Network for Interpretable Classification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.Markdown
[Yoo et al. "Relevance Regularization of Convolutional Neural Network for Interpretable Classification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/yoo2019cvprw-relevance/)BibTeX
@inproceedings{yoo2019cvprw-relevance,
title = {{Relevance Regularization of Convolutional Neural Network for Interpretable Classification}},
author = {Yoo, Chae Hwa and Kim, Na-Young and Kang, Je-Won},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2019},
pages = {40-43},
url = {https://mlanthology.org/cvprw/2019/yoo2019cvprw-relevance/}
}