{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T06:32:49Z","timestamp":1782282769092,"version":"3.54.5"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030289539","type":"print"},{"value":"9783030289546","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-28954-6_8","type":"book-chapter","created":{"date-parts":[[2019,9,9]],"date-time":"2019-09-09T19:08:50Z","timestamp":1568056130000},"page":"149-167","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Explanations for Attributing Deep Neural Network Predictions"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8831-6402","authenticated-orcid":false,"given":"Ruth","family":"Fong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1374-2858","authenticated-orcid":false,"given":"Andrea","family":"Vedaldi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,9,10]]},"reference":[{"key":"8_CR1","unstructured":"Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., Kim, B.: Sanity checks for saliency maps. In: NeurIPS, pp. 9525\u20139536 (2018)"},{"key":"8_CR2","doi-asserted-by":"crossref","unstructured":"Ancona, M., Ceolini, E., \u00d6ztireli, C., Gross, M.: Towards better understanding of gradient-based attribution methods for deep neural networks. In: ICLR (2018)","DOI":"10.1007\/978-3-030-28954-6_9"},{"issue":"8","key":"8_CR3","doi-asserted-by":"publisher","first-page":"e0181142","DOI":"10.1371\/journal.pone.0181142","volume":"12","author":"L Arras","year":"2017","unstructured":"Arras, L., Horn, F., Montavon, G., M\u00fcller, K.R., Samek, W.: \u201cWhat is relevant in a text document?\u201d: an interpretable machine learning approach. PLoS ONE 12(8), e0181142 (2017)","journal-title":"PLoS ONE"},{"issue":"7","key":"8_CR4","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"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Cao, C., et al.: Look and think twice: capturing top-down visual attention with feedback convolutional neural networks. In: ICCV, pp. 2956\u20132964 (2015)","DOI":"10.1109\/ICCV.2015.338"},{"key":"8_CR6","unstructured":"Dabkowski, P., Gal, Y.: Real time image saliency for black box classifiers. In: NIPS, pp. 6967\u20136976 (2017)"},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"Fong, R., Vedaldi, A.: Net2vec: quantifying and explaining how concepts are encoded by filters in deep neural networks. In: CVPR, pp. 8730\u20138738 (2018)","DOI":"10.1109\/CVPR.2018.00910"},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Fong, R.C., Vedaldi, A.: Interpretable explanations of black boxes by meaningful perturbation. In: ICCV, pp. 3429\u20133437 (2017)","DOI":"10.1109\/ICCV.2017.371"},{"key":"8_CR9","unstructured":"Greydanus, S., Koul, A., Dodge, J., Fern, A.: Visualizing and understanding atari agents. arXiv preprint \narXiv:1711.00138\n\n (2017)"},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Jia, Y., et al.: Caffe: Convolutional architecture for fast feature embedding. arXiv preprint \narXiv:1408.5093\n\n (2014)","DOI":"10.1145\/2647868.2654889"},{"key":"8_CR11","unstructured":"Kingma, D., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint \narXiv:1412.6980\n\n (2014)"},{"key":"8_CR12","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1097\u20131105 (2012)"},{"key":"8_CR13","unstructured":"Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial examples in the physical world. arXiv preprint \narXiv:1607.02533\n\n (2016)"},{"key":"8_CR14","doi-asserted-by":"crossref","unstructured":"Lenc, K., Vedaldi, A.: Understanding image representations by measuring their equivariance and equivalence. In: CVPR, pp. 991\u2013999 (2015)","DOI":"10.1109\/CVPR.2015.7298701"},{"key":"8_CR15","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":"8_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/978-3-319-46466-4_8","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Mahendran","year":"2016","unstructured":"Mahendran, A., Vedaldi, A.: Salient deconvolutional networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 120\u2013135. Springer, Cham (2016). \nhttps:\/\/doi.org\/10.1007\/978-3-319-46466-4_8"},{"key":"8_CR17","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.R.: Methods for interpreting and understanding deep neural networks. Digital Sig. Process. 73, 1\u201315 (2018)","journal-title":"Digital Sig. Process."},{"key":"8_CR18","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: CVPR, pp. 427\u2013436 (2015)","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"8_CR19","unstructured":"Petsiuk, V., Das, A., Saenko, K.: Rise: randomized input sampling for explanation of black-box models. In: BMVC (2018)"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Why should I trust you?: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144. ACM (2016)","DOI":"10.1145\/2939672.2939778"},{"issue":"11","key":"8_CR21","doi-asserted-by":"publisher","first-page":"2660","DOI":"10.1109\/TNNLS.2016.2599820","volume":"28","author":"W Samek","year":"2017","unstructured":"Samek, W., Binder, A., Montavon, G., Lapuschkin, S., M\u00fcller, K.R.: Evaluating the visualization of what a deep neural network has learned. IEEE Trans. Neural Netw. Learn. Syst. 28(11), 2660\u20132673 (2017)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"8_CR22","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., Batra, D.: Grad-cam: Why did you say that? visual explanations from deep networks via gradient-based localization. arXiv preprint \narXiv:1610.02391\n\n (2016)","DOI":"10.1109\/ICCV.2017.74"},{"key":"8_CR23","unstructured":"Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. arXiv preprint \narXiv:1704.02685\n\n (2017)"},{"key":"8_CR24","unstructured":"Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. In: ICLR (2014)"},{"key":"8_CR25","unstructured":"Smilkov, D., Thorat, N., Kim, B., Vi\u00e9gas, F., Wattenberg, M.: Smoothgrad: removing noise by adding noise. arXiv preprint \narxiv:1706.03825\n\n (2017)"},{"key":"8_CR26","unstructured":"Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: the all convolutional net. arXiv preprint \narXiv:1412.6806\n\n (2014)"},{"key":"8_CR27","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: ICML, pp. 3319\u20133328 (2017)"},{"key":"8_CR28","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: CVPR, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"8_CR29","doi-asserted-by":"crossref","unstructured":"Turner, R.: A model explanation system. In: IEEE MLSP, pp. 1\u20136 (2016)","DOI":"10.1109\/MLSP.2016.7738872"},{"key":"8_CR30","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). \nhttps:\/\/doi.org\/10.1007\/978-3-319-10590-1_53"},{"key":"8_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1007\/978-3-319-46493-0_33","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Zhang","year":"2016","unstructured":"Zhang, J., Lin, Z., Brandt, J., Shen, X., Sclaroff, S.: Top-down neural attention by excitation backprop. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 543\u2013559. Springer, Cham (2016). \nhttps:\/\/doi.org\/10.1007\/978-3-319-46493-0_33"},{"key":"8_CR32","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Object detectors emerge in deep scene CNNs. arXiv preprint \narXiv:1412.6856\n\n (2014)"},{"key":"8_CR33","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Lecture Notes in Computer Science","Explainable AI: Interpreting, Explaining and Visualizing Deep Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-28954-6_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,13]],"date-time":"2019-11-13T20:17:38Z","timestamp":1573676258000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-28954-6_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030289539","9783030289546"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-28954-6_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"10 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}