@inproceedings{4c234eb02db04abc8203b236394685f6,
title = "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.",
author = "Yoo, {Chae Hwa} and Nayoung Kim and Kang, {Je Won}",
note = "Funding Information: This work has supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(No.NRF-2019R1C1C1010249) Publisher Copyright: {\textcopyright} 2019 IEEE Computer Society. All rights reserved.; null ; Conference date: 16-06-2019 Through 20-06-2019",
year = "2019",
month = jun,
language = "English",
series = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",
publisher = "IEEE Computer Society",
pages = "40--43",
booktitle = "Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019",
}