{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T14:10:10Z","timestamp":1755871810188,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":43,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T00:00:00Z","timestamp":1704326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,1,4]]},"DOI":"10.1145\/3632410.3632425","type":"proceedings-article","created":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T18:15:16Z","timestamp":1704305716000},"page":"109-117","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["SCE: Shared Concept Extractor to Explain a CNN's Classification Dynamics"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7588-6318","authenticated-orcid":false,"given":"Vidhya","family":"Kamakshi","sequence":"first","affiliation":[{"name":"Department of Computer Science &amp; Engineering, Indian Institute of Technology Ropar, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6132-0310","authenticated-orcid":false,"given":"Narayanan","family":"C Krishnan","sequence":"additional","affiliation":[{"name":"Department of Data Science, Indian Institute of Technology Palakkad, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,1,4]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/0010-0277(83)90012-4"},{"key":"e_1_3_2_2_2_1","volume-title":"Recognition-by-components: a theory of human image understanding.Psychological review 94, 2","author":"Biederman Irving","year":"1987","unstructured":"Irving Biederman. 1987. Recognition-by-components: a theory of human image understanding.Psychological review 94, 2 (1987), 115."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.5201\/ipol.2011.bcm_nlm"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/make3040048"},{"key":"e_1_3_2_2_5_1","unstructured":"Chaofan Chen Oscar Li Daniel Tao Alina Barnett Cynthia Rudin and Jonathan\u00a0K Su. 2019. This looks like that: deep learning for interpretable image recognition. In Advances in Neural Information Processing Systems. 8928\u20138939."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1587\/transfun.E92.A.708"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1162\/NECO_a_00168"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.371"},{"key":"e_1_3_2_2_9_1","unstructured":"Amirata Ghorbani James Wexler James\u00a0Y Zou and Been Kim. 2019. Towards automatic concept-based explanations. In Advances in Neural Information Processing Systems. 9277\u20139286."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00047"},{"key":"e_1_3_2_2_11_1","volume-title":"Metrics for explainable AI: Challenges and prospects. arXiv preprint arXiv:1812.04608","author":"Hoffman R","year":"2018","unstructured":"Robert\u00a0R Hoffman, Shane\u00a0T Mueller, Gary Klein, and Jordan Litman. 2018. Metrics for explainable AI: Challenges and prospects. arXiv preprint arXiv:1812.04608 (2018)."},{"key":"e_1_3_2_2_12_1","volume-title":"Is ProtoPNet Really Explainable? Evaluating and Improving the Interpretability of Prototypes. arXiv preprint arXiv:2212.05946","author":"Huang Qihan","year":"2022","unstructured":"Qihan Huang, Mengqi Xue, Haofei Zhang, Jie Song, and Mingli Song. 2022. Is ProtoPNet Really Explainable? Evaluating and Improving the Interpretability of Prototypes. arXiv preprint arXiv:2212.05946 (2022)."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9534369"},{"key":"e_1_3_2_2_14_1","volume-title":"International Conference on Machine Learning. PMLR, 2668\u20132677","author":"Kim Been","year":"2018","unstructured":"Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV). In International Conference on Machine Learning. PMLR, 2668\u20132677."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3111138"},{"key":"e_1_3_2_2_16_1","volume-title":"Building machines that learn and think like people. Behavioral and brain sciences 40","author":"Lake M","year":"2017","unstructured":"Brenden\u00a0M Lake, Tomer\u00a0D Ullman, Joshua\u00a0B Tenenbaum, and Samuel\u00a0J Gershman. 2017. Building machines that learn and think like people. Behavioral and brain sciences 40 (2017), e253."},{"key":"e_1_3_2_2_17_1","volume-title":"Algorithms for non-negative matrix factorization. Advances in neural information processing systems 13","author":"Lee Daniel","year":"2000","unstructured":"Daniel Lee and H\u00a0Sebastian Seung. 2000. Algorithms for non-negative matrix factorization. Advances in neural information processing systems 13 (2000)."},{"key":"e_1_3_2_2_18_1","volume-title":"Learning the parts of objects by non-negative matrix factorization. Nature 401, 6755","author":"Lee D","year":"1999","unstructured":"Daniel\u00a0D Lee and H\u00a0Sebastian Seung. 1999. Learning the parts of objects by non-negative matrix factorization. Nature 401, 6755 (1999), 788\u2013791."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASPAA.2011.6082314"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.3390\/make2040034"},{"key":"e_1_3_2_2_21_1","unstructured":"Scott\u00a0M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems. 4765\u20134774."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3135797"},{"key":"e_1_3_2_2_23_1","first-page":"1","article-title":"Eigen-CAM: Visual Explanations for Deep Convolutional Neural Networks","volume":"2","author":"Muhammad Mohammed\u00a0Bany","year":"2021","unstructured":"Mohammed\u00a0Bany Muhammad and Mohammed Yeasin. 2021. Eigen-CAM: Visual Explanations for Deep Convolutional Neural Networks. SN Computer Science 2, 1 (2021), 1\u201314.","journal-title":"SN Computer Science"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-93736-2_34"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01469"},{"key":"e_1_3_2_2_26_1","volume-title":"Spurious Features Everywhere\u2013Large-Scale Detection of Harmful Spurious Features in ImageNet. arXiv preprint arXiv:2212.04871","author":"Neuhaus Yannic","year":"2022","unstructured":"Yannic Neuhaus, Maximilian Augustin, Valentyn Boreiko, and Matthias Hein. 2022. Spurious Features Everywhere\u2013Large-Scale Detection of Harmful Spurious Features in ImageNet. arXiv preprint arXiv:2212.04871 (2022)."},{"key":"e_1_3_2_2_27_1","volume-title":"The Building Blocks of Interpretability. Distill","author":"Olah Chris","year":"2018","unstructured":"Chris Olah, Arvind Satyanarayan, Ian Johnson, Shan Carter, Ludwig Schubert, Katherine Ye, and Alexander Mordvintsev. 2018. The Building Blocks of Interpretability. Distill (2018)."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01052"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19775-8_21"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467245"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP46576.2022.9897617"},{"key":"e_1_3_2_2_34_1","volume-title":"MAIRE-A Model-Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers. In International Cross-Domain Conference for Machine Learning and Knowledge Extraction. Springer, 329\u2013349","author":"Sharma Rajat","year":"2021","unstructured":"Rajat Sharma, Nikhil Reddy, Vidhya Kamakshi, Narayanan\u00a0C Krishnan, and Shweta Jain. 2021. MAIRE-A Model-Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers. In International Cross-Domain Conference for Machine Learning and Knowledge Extraction. Springer, 329\u2013349."},{"key":"e_1_3_2_2_35_1","volume-title":"International Conference on Learning Representations Workshop 7","author":"Springenberg Jost\u00a0Tobias","year":"2015","unstructured":"Jost\u00a0Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller. 2015. Striving for simplicity: The all convolutional net. International Conference on Learning Representations Workshop 7 (2015)."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00767-6_33"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.3390\/make3030032"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00093"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00093"},{"key":"e_1_3_2_2_40_1","volume-title":"Learning Reliable Visual Saliency for Model Explanations","author":"Wang Yulong","year":"2019","unstructured":"Yulong Wang, Hang Su, Bo Zhang, and Xiaolin Hu. 2019. Learning Reliable Visual Saliency for Model Explanations. IEEE Transactions on Multimedia (2019)."},{"key":"e_1_3_2_2_41_1","unstructured":"Chih-Kuan Yeh Been Kim Sercan Arik Chun-Liang Li Tomas Pfister and Pradeep Ravikumar. 2020. On Completeness-aware Concept-Based Explanations in Deep Neural Networks. In Advances in Neural Information Processing Systems Vol.\u00a033. 20554\u201320565."},{"key":"e_1_3_2_2_42_1","volume-title":"Improving Interpretability of CNN Models Using Non-Negative Concept Activation Vectors. In AAAI Conference on Artificial Intelligence.","author":"Zhang Ruihan","year":"2021","unstructured":"Ruihan Zhang, Prashan Madumal, Tim Miller, Kris Ehinger, and Benjamin Rubinstein. 2021. Improving Interpretability of CNN Models Using Non-Negative Concept Activation Vectors. In AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"}],"event":{"name":"CODS-COMAD 2024: 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)","acronym":"CODS-COMAD 2024","location":"Bangalore India"},"container-title":["Proceedings of the 7th Joint International Conference on Data Science &amp; Management of Data (11th ACM IKDD CODS and 29th COMAD)"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3632410.3632425","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3632410.3632425","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T13:35:53Z","timestamp":1755869753000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3632410.3632425"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,4]]},"references-count":43,"alternative-id":["10.1145\/3632410.3632425","10.1145\/3632410"],"URL":"https:\/\/doi.org\/10.1145\/3632410.3632425","relation":{},"subject":[],"published":{"date-parts":[[2024,1,4]]},"assertion":[{"value":"2024-01-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}