{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:05:31Z","timestamp":1740107131063,"version":"3.37.3"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T00:00:00Z","timestamp":1668211200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T00:00:00Z","timestamp":1668211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s00371-022-02715-8","type":"journal-article","created":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T04:29:27Z","timestamp":1668227367000},"page":"6097-6113","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Visual explanation and robustness assessment optimization of saliency maps for image classification"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7553-5558","authenticated-orcid":false,"given":"Xiaoshun","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinqiu","family":"Mo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,12]]},"reference":[{"key":"2715_CR1","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1007\/s00371-020-01901-w","volume":"37","author":"T Yang","year":"2021","unstructured":"Yang, T., Zhang, T., Huang, L.: Detection of defects in voltage-dependent resistors using stacked-block-based convolutional neural networks. Vis. Comput. 37, 1559\u20131567 (2021). https:\/\/doi.org\/10.1007\/s00371-020-01901-w","journal-title":"Vis. Comput."},{"key":"2715_CR2","doi-asserted-by":"publisher","unstructured":"Patel, N., Mukherjee, S., Ying, L.: EREL-Net: A remedy for industrial bottle defect detection. International Conference on Software Maintenance. Lecture Notes in Computer Science, vol 11010. Springer, Cham. (2018). https:\/\/doi.org\/10.1007\/978-3-030-04375-9_39","DOI":"10.1007\/978-3-030-04375-9_39"},{"key":"2715_CR3","doi-asserted-by":"publisher","unstructured":"Paleyes, A., Urma, R.G., Lawrence, N.D.: Challenges in deploying machine learning: a survey of case studies. NeurIPS: ML Retrospectives, Surveys & Meta-Analyses (2020). https:\/\/doi.org\/10.1145\/3533378","DOI":"10.1145\/3533378"},{"key":"2715_CR4","doi-asserted-by":"publisher","unstructured":"Gunning, D., Aha, D.: DARPA\u2019s explainable artificial intelligence (XAI) program. AI Magazine, vol. 40, no. 2 (2019). https:\/\/doi.org\/10.1609\/aimag.v40i2.2850","DOI":"10.1609\/aimag.v40i2.2850"},{"key":"2715_CR5","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138\u201352160 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2870052","journal-title":"IEEE Access"},{"key":"2715_CR6","unstructured":"Artificial Intelligence (AI) - Assessment of the robustness of neural networks. ISO\/IEC Technical Report 24029\u20131:2021 (2021)"},{"key":"2715_CR7","doi-asserted-by":"publisher","unstructured":"Martin, D., Heinzel, S., Von Bischhoffshausen, J. Kunze, K\u00fchl, N.: Deep learning strategies for industrial surface defect detection systems. In: the Annual Hawaii International Conference on System Sciences (2022). https:\/\/doi.org\/10.24251\/hicss.2022.146","DOI":"10.24251\/hicss.2022.146"},{"key":"2715_CR8","doi-asserted-by":"publisher","unstructured":"Vermeire, T., Laugel, T., Renard, X., Martens, D., Detyniecki, M.: How to choose an explainability method? Towards a methodical implementation of XAI in practice. Communications in Computer and Information Science, (2021). https:\/\/doi.org\/10.1007\/978-3-030-93736-2_39","DOI":"10.1007\/978-3-030-93736-2_39"},{"key":"2715_CR9","unstructured":"Brundage, M. et al.: Toward trustworthy AI development: mechanisms for supporting verifiable claims. arXiv preprint arXiv: 2004.07213v2 (2020)"},{"key":"2715_CR10","doi-asserted-by":"publisher","unstructured":"Wagner, J., K\u00f6hler, J. M., Gindele, T., Hetzel, L., Wiedemer, J. T., Behnke, S.: Interpretable and fine-grained visual explanations for convolutional neural networks. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9089\u20139099 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00931","DOI":"10.1109\/CVPR.2019.00931"},{"issue":"10","key":"2715_CR11","first-page":"2071","volume":"56","author":"S Ji","year":"2019","unstructured":"Ji, S., Li, J., Du, T., Li, B.: A survey on techniques, applications and security of machine learning interpretability. J. Comput. Res. Develop. 56(10), 2071\u20132096 (2019)","journal-title":"J. Comput. Res. Develop."},{"key":"2715_CR12","doi-asserted-by":"publisher","unstructured":"Khorram, S., Lawson, T., Li, F.: iGOS++: integrated gradient optimized saliency by bilateral perturbations. CHIL \u201921: Proceedings of the Conference on Health, Inference, and Learning April, Pages 174\u2013182. (2021). https:\/\/doi.org\/10.1145\/3450439.3451865","DOI":"10.1145\/3450439.3451865"},{"key":"2715_CR13","unstructured":"Finale, D., Kim, B.: Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608v2 (2017)"},{"key":"2715_CR14","unstructured":"Springenberg, J., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: the all convolutional net. arXiv preprint arXiv:1412.6806 (2014)"},{"key":"2715_CR15","doi-asserted-by":"crossref","unstructured":"Zeiler, M. D., Fergus, R.: Visualizing and understanding convolutional networks. In European conference on computer vision, pp. 818\u2013833. Springer (2014)","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"2715_CR16","unstructured":"Simonyan, K., Vedaldi, A., Zisserman A.: Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013)"},{"key":"2715_CR17","unstructured":"Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., Lipson, H.: Understanding neural networks through deep visualization. arXiv preprint arXiv:1506.06579 (2015)"},{"key":"2715_CR18","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you?: Explaining the predictions of any classifier. In Proc. of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"2715_CR19","doi-asserted-by":"publisher","unstructured":"Fong, R. C., Vedaldi, A.: Interpretable explanations of black boxes by meaningful perturbation. 2017 IEEE International Conference on Computer Vision (ICCV), pp. 3449\u20133457 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.371","DOI":"10.1109\/ICCV.2017.371"},{"key":"2715_CR20","unstructured":"Petsiuk, V., Das, A., Saenko, K.: RISE: Randomized input sampling for explanation of black-box models. In: British Machine Vision Conference (2018)"},{"key":"2715_CR21","doi-asserted-by":"crossref","unstructured":"Ribeiro, M. T., Singh, S., Guestrin, C.: Anchors: high-precision model-agnostic explanations. In: AAAI Conference on Artificial Intelligence, pp 1527\u20131535 (2018)","DOI":"10.1609\/aaai.v32i1.11491"},{"key":"2715_CR22","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"A Barredo Arrieta","year":"2020","unstructured":"Barredo Arrieta, A., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inform. Fus. 58, 82\u2013115 (2020). https:\/\/doi.org\/10.1016\/j.inffus.2019.12.012","journal-title":"Inform. Fus."},{"key":"2715_CR23","doi-asserted-by":"publisher","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2921\u20132929 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.319","DOI":"10.1109\/CVPR.2016.319"},{"key":"2715_CR24","doi-asserted-by":"publisher","unstructured":"Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: Visual explanations from deep networks via gradient-based localization. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 618\u2013626 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.74","DOI":"10.1109\/ICCV.2017.74"},{"key":"2715_CR25","doi-asserted-by":"publisher","unstructured":"Wang, H. et al.: Score-CAM: score-weighted visual explanations for convolutional neural networks. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020, pp. 111\u2013119, (2020). https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00020.","DOI":"10.1109\/CVPRW50498.2020.00020"},{"key":"2715_CR26","doi-asserted-by":"publisher","first-page":"1208","DOI":"10.7544\/ISSN1000-1239.2020.20190485","volume":"57","author":"K Cheng","year":"2020","unstructured":"Cheng, K., Wang, N., Shi, W., Zhan, Y.: Research advances in the interpretability of deep learning. J. Comput. Res. Develop. 57, 1208 (2020). https:\/\/doi.org\/10.7544\/ISSN1000-1239.2020.20190485","journal-title":"J. Comput. Res. Develop."},{"issue":"5","key":"2715_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3236009","volume":"51","author":"R Guidotti","year":"2018","unstructured":"Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. 51(5), 1\u201342 (2018). https:\/\/doi.org\/10.1145\/3236009","journal-title":"ACM Comput. Surv."},{"key":"2715_CR28","doi-asserted-by":"publisher","unstructured":"Fong, R., Patrick, M., Vedaldi A.: Understanding deep networks via extremal perturbations and smooth masks. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 2950\u20132958 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00304","DOI":"10.1109\/ICCV.2019.00304"},{"key":"2715_CR29","doi-asserted-by":"publisher","unstructured":"Li, X., Shi, Y., Li, H., Bai, W., Song, Y., Cao, C., Chen, L.: An experimental study of quantitative evaluations on saliency methods. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Association for Computing Machinery, New York, NY, USA, 3200\u20133208 (2021). https:\/\/doi.org\/10.1145\/3447548.3467148","DOI":"10.1145\/3447548.3467148"},{"key":"2715_CR30","volume-title":"Visual cues: practical data visualization","author":"PR Keller","year":"1993","unstructured":"Keller, P.R., Keller, M.M.: Visual cues: practical data visualization. IEEE Computer Society Press, Los Alamitos (1993)"},{"key":"2715_CR31","unstructured":"Chen, W., Zhang, S., Lu, A., Zhao, Y.: Guide for Data Visualization (In Chinese). High Education Press (2020)"},{"issue":"1\/2","key":"2715_CR32","doi-asserted-by":"publisher","first-page":"149","DOI":"10.2307\/2332539","volume":"36","author":"NL Johnson","year":"1949","unstructured":"Johnson, N.L.: Systems of frequency curves generated by methods of translation. Biometrika 36(1\/2), 149 (1949). https:\/\/doi.org\/10.2307\/2332539","journal-title":"Biometrika"},{"key":"2715_CR33","doi-asserted-by":"publisher","unstructured":"Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3D object representations for fine-grained categorization. In: 2013 IEEE International Conference on Computer Vision Workshops, pp. 554\u2013561 (2013). https:\/\/doi.org\/10.1109\/ICCVW.2013.77","DOI":"10.1109\/ICCVW.2013.77"},{"key":"2715_CR34","unstructured":"Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. In: Proc. of International Conference on Machine Learning, pp. 6105\u20136114 (2019)"},{"key":"2715_CR35","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06282-2","author":"DA Morales","year":"2020","unstructured":"Morales, D.A., Talavera, E., Remeseiro, B.: Playing to distraction: towards a robust training of cnn classifiers through visual explanation techniques. Neural Comput. Appl. (2020). https:\/\/doi.org\/10.1007\/s00521-021-06282-2","journal-title":"Neural Comput. Appl."},{"key":"2715_CR36","doi-asserted-by":"publisher","DOI":"10.4324\/9781315009292","volume-title":"Principles of Gestalt psychology","author":"K Koffka","year":"2013","unstructured":"Koffka, K.: Principles of Gestalt psychology. Routledge, Taylor & Francis Group, London (2013)"},{"key":"2715_CR37","doi-asserted-by":"crossref","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K. Q.: On calibration of modern neural networks. In: Proceedings of the 34th International Conference on Machine Learning, 70:1321\u20131330 (2017)","DOI":"10.1109\/WACV.2018.00149"},{"key":"2715_CR38","first-page":"23296","volume":"34","author":"M Naseer","year":"2021","unstructured":"Naseer, M., Ranasinghe, K., et al.: Intriguing properties of vision transformers. Neural Inform. Process. Syst. (NeurIPS 2021) 34, 23296\u201323308 (2021)","journal-title":"Neural Inform. Process. Syst. (NeurIPS 2021)"},{"key":"2715_CR39","doi-asserted-by":"publisher","unstructured":"Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 07, pp. 13001\u201313008 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i07.7000","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"2715_CR40","doi-asserted-by":"publisher","unstructured":"Yun, S., Han, D., Chun, S., Oh, S. J., Yoo, Y., Choe, J.: CutMix: regularization strategy to train strong classifiers with localizable features. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 6022\u20136031 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00612","DOI":"10.1109\/ICCV.2019.00612"},{"issue":"3","key":"2715_CR41","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int. J. Comput. Vision"},{"key":"2715_CR42","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"issue":"11","key":"2715_CR43","doi-asserted-by":"publisher","first-page":"4196","DOI":"10.1109\/TPAMI.2021.3054303","volume":"43","author":"SA Bargal","year":"2021","unstructured":"Bargal, S.A., et al.: Guided zoom: zooming into network evidence to refine fine-grained model decisions. IEEE Transactions Pattern Anal. Mach. Intell. 43(11), 4196\u20134202 (2021). https:\/\/doi.org\/10.1109\/TPAMI.2021.3054303","journal-title":"IEEE Transactions Pattern Anal. Mach. Intell."},{"key":"2715_CR44","doi-asserted-by":"publisher","unstructured":"Du, R. et al.: Fine-grained visual classification via progressive multi-granularity training of jigsaw patches. Computer Vision \u2013 ECCV 2020. Lecture Notes in Computer Science, vol 12365. Springer, Cham. (2020). https:\/\/doi.org\/10.1007\/978-3-030-58565-5_10","DOI":"10.1007\/978-3-030-58565-5_10"},{"key":"2715_CR45","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-022-02506-1","author":"H Pei","year":"2022","unstructured":"Pei, H., Guo, R., Tan, Z., et al.: Fine-grained classification of automobile front face modeling based on Gestalt psychology. Vis. Comput. (2022). https:\/\/doi.org\/10.1007\/s00371-022-02506-1","journal-title":"Vis. Comput."},{"issue":"3","key":"2715_CR46","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1109\/TPAMI.2018.2815601","volume":"41","author":"Z Bylinskii","year":"2019","unstructured":"Bylinskii, Z., Judd, T., Oliva, A., Torralba, A., Durand, F.: What do different evaluation metrics tell us about saliency models. IEEE Transactions Pattern Anal. Mach. Intell. 41(3), 740\u2013757 (2019). https:\/\/doi.org\/10.1109\/TPAMI.2018.2815601","journal-title":"IEEE Transactions Pattern Anal. Mach. Intell."},{"key":"2715_CR47","doi-asserted-by":"publisher","unstructured":"Riche N, Duvinage M, Mancas M, Gosselin B, Dutoit T.: Saliency and human fixations: state-of-the-art and study of comparison metrics. In IEEE International Conference on Computer Vision, pp. 1153\u20131160 (2013). https:\/\/doi.org\/10.1109\/ICCV.2013.147","DOI":"10.1109\/ICCV.2013.147"},{"issue":"10","key":"2715_CR48","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1016\/j.imavis.2013.08.004","volume":"31","author":"M Emami","year":"2013","unstructured":"Emami, M., Hoberock, L.L.: Selection of a best metric and evaluation of bottom-up visual saliency models. Image Vis. Comput. 31(10), 796\u2013808 (2013). https:\/\/doi.org\/10.1016\/j.imavis.2013.08.004","journal-title":"Image Vis. Comput."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-022-02715-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-022-02715-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-022-02715-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T01:24:35Z","timestamp":1728350675000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-022-02715-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,12]]},"references-count":48,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["2715"],"URL":"https:\/\/doi.org\/10.1007\/s00371-022-02715-8","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"type":"print","value":"0178-2789"},{"type":"electronic","value":"1432-2315"}],"subject":[],"published":{"date-parts":[[2022,11,12]]},"assertion":[{"value":"20 October 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}