{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T17:42:28Z","timestamp":1772905348104,"version":"3.50.1"},"reference-count":40,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001381","name":"National Research Foundation, Singapore","doi-asserted-by":"publisher","award":["AISG-GC-2019-001,AISG-RP-2018-008"],"award-info":[{"award-number":["AISG-GC-2019-001,AISG-RP-2018-008"]}],"id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,18]]},"DOI":"10.1109\/ijcnn52387.2021.9534269","type":"proceedings-article","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T20:32:37Z","timestamp":1632342757000},"page":"1-8","source":"Crossref","is-referenced-by-count":20,"title":["Comprehensible Convolutional Neural Networks via Guided Concept Learning"],"prefix":"10.1109","author":[{"given":"Sandareka","family":"Wickramanayake","sequence":"first","affiliation":[{"name":"National University of Singapore,School of Computing"}]},{"given":"Wynne","family":"Hsu","sequence":"additional","affiliation":[{"name":"National University of Singapore,School of Computing"}]},{"given":"Mong Li","family":"Lee","sequence":"additional","affiliation":[{"name":"National University of Singapore,School of Computing"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2857768"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00842"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240523"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.13"},{"key":"ref31","author":"wah","year":"2011","journal-title":"Caltech-used birds-200–2011 dataset"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01216-8_17"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00515"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00869"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.254"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33012539"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298658"},{"key":"ref11","article-title":"This looks like that: deep learning for interpretable image recognition","author":"chen","year":"0","journal-title":"NeurIPS"},{"key":"ref12","article-title":"Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions","author":"li","year":"0","journal-title":"AAAI"},{"key":"ref13","article-title":"Towards robust interpretability with self-explaining neural networks","author":"melis","year":"0","journal-title":"NeurIPS"},{"key":"ref14","article-title":"Visualizing and understanding convolutional networks","author":"zeiler","year":"0","journal-title":"ECCV"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.354"},{"key":"ref16","article-title":"Tcav: Relative concept importance testing with linear concept activation vectors","author":"kim","year":"2017","journal-title":"ArXiv"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0130140"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01237-3_8"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00920"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref3","article-title":"Ask, attend and answer: Exploring question-guided spatial attention for visual question answering","author":"xu","year":"0","journal-title":"ECCV"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_1"},{"key":"ref29","author":"salton","year":"1986","journal-title":"Introduction to Modern Information Retrieval"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.23915\/distill.00010"},{"key":"ref8","article-title":"An unexpected unity among methods for interpreting model predictions","author":"lundberg","year":"0","journal-title":"NeurIPS"},{"key":"ref7","article-title":"Understanding black-box predictions via influence functions","author":"koh","year":"0","journal-title":"ICML"},{"key":"ref2","article-title":"Show, Attend and Tell: Neural Image Caption Generation with Visual Attention","author":"xu","year":"0","journal-title":"ICML"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00915"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298935"},{"key":"ref20","article-title":"Towards automatic concept-based explanations","author":"ghorbani","year":"0","journal-title":"NeurIPS"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref21","article-title":"On concept-based explanations in deep neural networks","author":"yeh","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00928"},{"key":"ref23","article-title":"Explainable models with consistent interpretations","author":"pillai","year":"0","journal-title":"AAAI"},{"key":"ref26","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"ArXiv Preprint"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00642"}],"event":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","location":"Shenzhen, China","start":{"date-parts":[[2021,7,18]]},"end":{"date-parts":[[2021,7,22]]}},"container-title":["2021 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9533266\/9533267\/09534269.pdf?arnumber=9534269","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T23:32:49Z","timestamp":1659483169000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9534269\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,18]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/ijcnn52387.2021.9534269","relation":{},"subject":[],"published":{"date-parts":[[2021,7,18]]}}}