{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T22:48:02Z","timestamp":1752101282986,"version":"3.37.3"},"reference-count":42,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2023,6,1]]},"DOI":"10.1109\/tpami.2022.3226041","type":"journal-article","created":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T20:49:53Z","timestamp":1669927793000},"page":"7625-7638","source":"Crossref","is-referenced-by-count":5,"title":["Optimising for Interpretability: Convolutional Dynamic Alignment Networks"],"prefix":"10.1109","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5479-3769","authenticated-orcid":false,"given":"Moritz","family":"B\u00f6hle","sequence":"first","affiliation":[{"name":"Department of Computer Vision and Machine Learning, Max Planck Institute for Informatics, Saarbrücken, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8949-9896","authenticated-orcid":false,"given":"Mario","family":"Fritz","sequence":"additional","affiliation":[{"name":"CISPA Helmholtz Center for Information Security, Saarbrücken, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9683-5237","authenticated-orcid":false,"given":"Bernt","family":"Schiele","sequence":"additional","affiliation":[{"name":"Department of Computer Vision and Machine Learning, Max Planck Institute for Informatics, Saarbrücken, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3390\/e23010018"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3627-6"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00990"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01465"},{"key":"ref15","article-title":"RISE: Randomized input sampling for explanation of black-box models","author":"petsiuk","year":"2018","journal-title":"Proc Brit Mach Vis Conf"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"},{"key":"ref14","first-page":"4768","article-title":"A unified approach to interpreting model predictions","author":"lundberg","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-08332-3"},{"article-title":"ResNet on tiny ImageNet","year":"2016","author":"sun","key":"ref31"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0130140"},{"year":"0","key":"ref33","article-title":"VGGNet and tiny ImageNet"},{"key":"ref10","article-title":"Full-gradient representation for neural network visualization","author":"srinivas","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.3390\/app10030884"},{"key":"ref2","first-page":"2924","article-title":"On the number of linear regions of deep neural networks","author":"mont\u00fafar","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref1","first-page":"807","article-title":"Rectified linear units improve restricted boltzmann machines","author":"nair","year":"2010","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref17","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref39","first-page":"1803","article-title":"How to explain individual classification decisions","volume":"11","author":"baehrens","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"ref38","first-page":"818","article-title":"Visualizing and understanding convolutional networks","author":"zeiler","year":"2014","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref24","first-page":"2048","article-title":"Show, attend and tell: Neural image caption generation with visual attention","author":"xu","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref23","first-page":"667","article-title":"Dynamic filter networks","author":"jia","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"article-title":"MNIST handwritten digit database","year":"1998","author":"lecun","key":"ref26"},{"key":"ref25","first-page":"3859","article-title":"Dynamic routing between capsules","author":"sabour","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref20","first-page":"8930","article-title":"This looks like that: Deep learning for interpretable image recognition","author":"chen","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"article-title":"Proper ResNet implementation for CIFAR10\/CIFAR100 in PyTorch","year":"0","author":"idelbayev","key":"ref42"},{"key":"ref41","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref22","first-page":"7786","article-title":"Towards robust interpretability with self-explaining neural networks","author":"alvarez-melis","year":"2018","journal-title":"Proc Adv Neural Inf Process"},{"key":"ref21","article-title":"Approximating CNNs with bag-of-local-features models works surprisingly well on ImageNet","author":"brendel","year":"2019","journal-title":"Proc Int Conf Learn Representations"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"krizhevsky","key":"ref28"},{"article-title":"Intuitively understanding convolutions for deep learning","year":"2018","author":"shafkat","key":"ref27"},{"article-title":"Tiny imagenet","year":"0","author":"mnmoustafa","key":"ref29"},{"key":"ref8","first-page":"3145","article-title":"Learning important features through propagating activation differences","author":"shrikumar","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref9","first-page":"3319","article-title":"Axiomatic attribution for deep networks","author":"sundararajan","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref4","article-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","author":"simonyan","year":"2014","journal-title":"Proc Int Conf Learn Representations Workshop"},{"key":"ref3","first-page":"9525","article-title":"Sanity checks for saliency maps","author":"adebayo","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref5","article-title":"Striving for simplicity: The all convolutional net","author":"springenberg","year":"2015","journal-title":"Proc Int Conf Learn Representations Workshop"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-017-1059-x"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10120646\/09968133.pdf?arnumber=9968133","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T17:34:58Z","timestamp":1685381698000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9968133\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,1]]},"references-count":42,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2022.3226041","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"type":"print","value":"0162-8828"},{"type":"electronic","value":"2160-9292"},{"type":"electronic","value":"1939-3539"}],"subject":[],"published":{"date-parts":[[2023,6,1]]}}}