{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T12:32:37Z","timestamp":1761395557915,"version":"3.40.3"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030306410"},{"type":"electronic","value":"9783030306427"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-30642-7_19","type":"book-chapter","created":{"date-parts":[[2019,9,4]],"date-time":"2019-09-04T06:12:17Z","timestamp":1567577537000},"page":"207-218","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Contrastive Explanations to Classification Systems Using Sparse Dictionaries"],"prefix":"10.1007","author":[{"given":"A.","family":"Apicella","sequence":"first","affiliation":[]},{"given":"F.","family":"Isgr\u00f2","sequence":"additional","affiliation":[]},{"given":"R.","family":"Prevete","sequence":"additional","affiliation":[]},{"given":"G.","family":"Tamburrini","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,2]]},"reference":[{"key":"19_CR1","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)","journal-title":"IEEE Access"},{"key":"19_CR2","doi-asserted-by":"crossref","unstructured":"Apicella, A., Isgr\u00f2, F., Prevete, R., Sorrentino, A., Tamburrini, G.: Explaining classification systems using sparse dictionaries. In: Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Special Session on Societal Issues in Machine Learning: When Learning from Data is Not Enough (2019)","DOI":"10.1007\/978-3-030-30642-7_19"},{"issue":"7","key":"19_CR3","doi-asserted-by":"publisher","first-page":"e0130140","DOI":"10.1371\/journal.pone.0130140","volume":"10","author":"S Bach","year":"2015","unstructured":"Bach, S., Binder, A., Montavon, G., Klauschen, F., M\u00fcller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS One 10(7), e0130140 (2015)","journal-title":"PloS One"},{"issue":"7","key":"19_CR4","doi-asserted-by":"publisher","first-page":"1356","DOI":"10.1109\/TPAMI.2015.2487966","volume":"38","author":"C Bao","year":"2016","unstructured":"Bao, C., Ji, H., Quan, Y., Shen, Z.: Dictionary learning for sparse coding: algorithms and convergence analysis. IEEE Trans. Pattern Anal. Mach. Intell. 38(7), 1356\u20131369 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-319-44781-0_8","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2016","author":"A Binder","year":"2016","unstructured":"Binder, A., Montavon, G., Lapuschkin, S., M\u00fcller, K.-R., Samek, W.: Layer-wise relevance propagation for neural networks with local renormalization layers. In: Villa, A.E.P., Masulli, P., Pons Rivero, A.J. (eds.) ICANN 2016. LNCS, vol. 9887, pp. 63\u201371. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-44781-0_8"},{"key":"19_CR6","doi-asserted-by":"crossref","unstructured":"Caruana, R., Lou, Y., et al.: Intelligible models for healthcare: predicting pneumonia risk and hospital 30-day readmission. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1721\u20131730. ACM (2015)","DOI":"10.1145\/2783258.2788613"},{"key":"19_CR7","doi-asserted-by":"crossref","unstructured":"Cohen, G., Afshar, S., Tapson, J., van Schaik, A.: EMNIST: an extension of MNIST to handwritten letters. arXiv e-prints arXiv:1702.05373 February 2017","DOI":"10.1109\/IJCNN.2017.7966217"},{"issue":"2","key":"19_CR8","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/S0933-3657(96)00367-3","volume":"9","author":"GF Cooper","year":"1997","unstructured":"Cooper, G.F., Aliferis, C.F., et al.: An evaluation of machine-learning methods for predicting pneumonia mortality. Artif. Intell. Med. 9(2), 107\u2013138 (1997)","journal-title":"Artif. Intell. Med."},{"key":"19_CR9","unstructured":"Doran, D., Schulz, S., Besold, T.R.: What does explainable ai really mean? A new conceptualization of perspectives. arXiv preprint arXiv:1710.00794 (2017)"},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., Brox, T.: Inverting visual representations with convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4829\u20134837 (2016)","DOI":"10.1109\/CVPR.2016.522"},{"key":"19_CR11","unstructured":"Erhan, D., Bengio, Y., Courville, A., Vincent, P.: Visualizing higher-layer features of a deep network. University of Montreal 1341(3), p. 1 (2009)"},{"issue":"5","key":"19_CR12","doi-asserted-by":"publisher","first-page":"93","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. (CSUR) 51(5), 93 (2018)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"1","key":"19_CR13","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1037\/0033-2909.107.1.65","volume":"107","author":"DJ Hilton","year":"1990","unstructured":"Hilton, D.J.: Conversational processes and causal explanation. Psychol. Bull. 107(1), 65 (1990)","journal-title":"Psychol. Bull."},{"issue":"Nov","key":"19_CR14","first-page":"1457","volume":"5","author":"PO Hoyer","year":"2004","unstructured":"Hoyer, P.O.: Non-negative matrix factorization with sparseness constraints. J. Mach. Learn. Res. 5(Nov), 1457\u20131469 (2004)","journal-title":"J. Mach. Learn. Res."},{"key":"19_CR15","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations, December 2014"},{"issue":"11","key":"19_CR16","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"19_CR17","unstructured":"Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Advances in neural information processing systems, pp. 556\u2013562 (2001)"},{"issue":"3","key":"19_CR18","doi-asserted-by":"publisher","first-page":"1350","DOI":"10.1214\/15-AOAS848","volume":"9","author":"B Letham","year":"2015","unstructured":"Letham, B., Rudin, C., McCormick, T.H., Madigan, D., et al.: Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model. Ann. Appl. Stat. 9(3), 1350\u20131371 (2015)","journal-title":"Ann. Appl. Stat."},{"key":"19_CR19","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1017\/S1358246100005130","volume":"27","author":"P Lipton","year":"1990","unstructured":"Lipton, P.: Contrastive explanation. Roy. Inst. Philos. Suppl. 27, 247\u2013266 (1990)","journal-title":"Roy. Inst. Philos. Suppl."},{"issue":"3","key":"19_CR20","doi-asserted-by":"crossref","first-page":"30:31","DOI":"10.1145\/3236386.3241340","volume":"16","author":"ZC Lipton","year":"2018","unstructured":"Lipton, Z.C.: The mythos of model interpretability. Queue 16(3), 30:31\u201330:57 (2018)","journal-title":"Queue"},{"key":"19_CR21","doi-asserted-by":"crossref","unstructured":"Mahendran, A., Vedaldi, A.: Understanding deep image representations by inverting them. In: CVPR, pp. 5188\u20135196 (2015)","DOI":"10.1109\/CVPR.2015.7299155"},{"key":"19_CR22","unstructured":"Mensch, A., Mairal, J., Thirion, B., Varoquaux, G.: Dictionary learning for massive matrix factorization. In: International Conference on Machine Learning, pp. 1737\u20131746 (2016)"},{"key":"19_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artint.2018.07.007","volume":"267","author":"T Miller","year":"2018","unstructured":"Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. 267, 1\u201338 (2018)","journal-title":"Artif.. Intell."},{"key":"19_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dsp.2017.10.011","volume":"73","author":"G Montavon","year":"2018","unstructured":"Montavon, G., Samek, W., M\u00fcller, K.: Methods for interpreting and understanding deep neural networks. Digital Signal Process. 73, 1\u201315 (2018)","journal-title":"Digital Signal Process."},{"key":"19_CR25","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.patcog.2016.11.008","volume":"65","author":"G Montavon","year":"2017","unstructured":"Montavon, G., Lapuschkin, S., Binder, A., Samek, W., M\u00fcller, K.R.: Explaining nonlinear classification decisions with deep taylor decomposition. Pattern Recogn. 65, 211\u2013222 (2017)","journal-title":"Pattern Recogn."},{"key":"19_CR26","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Clune, J., Bengio, Y., Dosovitskiy, A., Yosinski, J.: Plug & play generative networks: Conditional iterative generation of images in latent space. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4467\u20134477 (2017)","DOI":"10.1109\/CVPR.2017.374"},{"key":"19_CR27","unstructured":"Nguyen, A., Dosovitskiy, A., Yosinski, J., Brox, T., Clune, J.: Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. In: Advances in Neural Information Processing Systems, pp. 3387\u20133395 (2016)"},{"key":"19_CR28","unstructured":"N\u00fa\u00f1ez, H., Angulo, C., Catal\u00e0, A.: Rule extraction from support vector machines. In: Esann, pp. 107\u2013112 (2002)"},{"key":"19_CR29","unstructured":"Prevete, R., Apicella, A., Isgr\u00f2, F., Tamburrini, G.: Explaining the behavior of learning classification systems: a black-box approach. In: Proceedings of the 15th Conference of the Italian Association for Cognitive Sciences (2018)"},{"key":"19_CR30","doi-asserted-by":"crossref","unstructured":"Qin, Z., Yu, F., Liu, C., Chen, X.: How convolutional neural network see the world-a survey of convolutional neural network visualization methods. arXiv preprint arXiv:1804.11191 (2018)","DOI":"10.3934\/mfc.2018008"},{"key":"19_CR31","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you? Explaining the predictions of any classifier. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144. ACM (2016)","DOI":"10.1145\/2939672.2939778"},{"issue":"5","key":"19_CR32","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1109\/TKDE.2007.190734","volume":"20","author":"M Robnik-\u0160ikonja","year":"2008","unstructured":"Robnik-\u0160ikonja, M., Kononenko, I.: Explaining classifications for individual instances. IEEE Trans. Knowl. Data Eng. 20(5), 589\u2013600 (2008)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"19_CR33","unstructured":"Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 3145\u20133153. JMLR. org (2017)"},{"key":"19_CR34","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":"19_CR35","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.jneumeth.2016.10.008","volume":"274","author":"I Sturm","year":"2016","unstructured":"Sturm, I., Lapuschkin, S., Samek, W., M\u00fcller, K.: Interpretable deep neural networks for single-trial eeg classification. J. Neurosci. Methods 274, 141\u2013145 (2016)","journal-title":"J. Neurosci. Methods"},{"key":"19_CR36","unstructured":"Szegedy, C., Zaremba, W., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)"},{"key":"19_CR37","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","volume-title":"Computer Vision \u2013 ECCV 2014","author":"MD Zeiler","year":"2014","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818\u2013833. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53"},{"key":"19_CR38","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., Taylor, G.W., Fergus, R.: Adaptive deconvolutional networks for mid and high level feature learning. In: ICCV, pp. 2018\u20132025 (2011)","DOI":"10.1109\/ICCV.2011.6126474"},{"key":"19_CR39","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1007\/s11263-017-1059-x","volume":"126","author":"J Zhang","year":"2017","unstructured":"Zhang, J., Bargal, S.A., et al.: Top-down neural attention by excitation backprop. Int. J. Comput. Vision 126, 1084\u20131102 (2017)","journal-title":"Int. J. Comput. Vision"},{"issue":"1","key":"19_CR40","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1631\/FITEE.1700808","volume":"19","author":"Q Zhang","year":"2018","unstructured":"Zhang, Q., Zhu, S.: Visual interpretability for deep learning: a survey. Front. Inf. Technol. Electron. Eng. 19(1), 27\u201339 (2018)","journal-title":"Front. Inf. Technol. Electron. Eng."},{"key":"19_CR41","unstructured":"Zintgraf, L.M., Cohen, T.S., Adel, T., Welling, M.: Visualizing deep neural network decisions: Prediction difference analysis. arXiv preprint arXiv:1702.04595 (2017)"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing \u2013 ICIAP 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30642-7_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T00:12:21Z","timestamp":1693786341000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-30642-7_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030306410","9783030306427"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30642-7_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"2 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Trento","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/event.unitn.it\/iciap2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"207","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"117","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"57% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.6","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}