{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:08:08Z","timestamp":1730246888857,"version":"3.28.0"},"reference-count":19,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,9,19]],"date-time":"2021-09-19T00:00:00Z","timestamp":1632009600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,9,19]],"date-time":"2021-09-19T00:00:00Z","timestamp":1632009600000},"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":[],"published-print":{"date-parts":[[2021,9,19]]},"DOI":"10.1109\/icip42928.2021.9506711","type":"proceedings-article","created":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T21:08:41Z","timestamp":1629752921000},"page":"3717-3721","source":"Crossref","is-referenced-by-count":1,"title":["Explainers in the Wild: Making Surrogate Explainers Robust to Distortions Through Perception"],"prefix":"10.1109","author":[{"given":"Alexander","family":"Hepburn","sequence":"first","affiliation":[{"name":"University of Bristol,Department of Engineering Mathematics,UK"}]},{"given":"Raul","family":"Santos-Rodriguez","sequence":"additional","affiliation":[{"name":"University of Bristol,Department of Engineering Mathematics,UK"}]}],"member":"263","reference":[{"key":"ref10","article-title":"blimey: surrogate prediction explanations beyond lime","author":"sokol","year":"2019","journal-title":"Workshop on Human-Centric Machine Learning (HCML 2019) 33rd Conference on Neural Information Processing Systems"},{"key":"ref11","article-title":"On the overlooked issue of defining explanation objectives for local-surrogate explainers","author":"poyiadzi","year":"2021","journal-title":"arXiv preprint arXiv 2106 01111"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/s13218-020-00637-y"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.2003.1292216"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.2352\/ISSN.2470-1173.2016.16.HVEI-103"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1364\/JOSAA.34.001511"},{"journal-title":"Digital Images and Human Vision","year":"1993","author":"watson","key":"ref16"},{"key":"ref17","first-page":"248","article-title":"Imagenet: A large-scale hierarchical image database","author":"deng","year":"2009","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.21105\/joss.01904"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3375627.3375850"},{"key":"ref3","article-title":"Approximating cnns with bag-of-local-features models works surprisingly well on imagenet","author":"brendel","year":"2019","journal-title":"arXiv preprint arXiv 1904 04370"},{"key":"ref6","article-title":"Benchmarking neural network robustness to common corruptions and perturbations","author":"hendrycks","year":"2019","journal-title":"arXiv preprint arXiv 1903 11593"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP40778.2020.9190691"},{"key":"ref7","article-title":"On the relation between statistical learning and perceptual distances","author":"hepburn","year":"2021","journal-title":"arXiv preprint arXiv 2106 04427"},{"key":"ref2","article-title":"This looks like that: deep learning for interpretable image recognition","author":"chen","year":"2018","journal-title":"arXiv preprint arXiv 1806 10976"},{"key":"ref9","article-title":"Enforcing perceptual consistency on generative adversarial networks by using the normalised laplacian pyramid distance","volume":"abs 1908 4347","author":"hepburn","year":"2019","journal-title":"CoRR"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0048-x"}],"event":{"name":"2021 IEEE International Conference on Image Processing (ICIP)","start":{"date-parts":[[2021,9,19]]},"location":"Anchorage, AK, USA","end":{"date-parts":[[2021,9,22]]}},"container-title":["2021 IEEE International Conference on Image Processing (ICIP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9506008\/9506009\/09506711.pdf?arnumber=9506711","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T21:18:21Z","timestamp":1655759901000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9506711\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,19]]},"references-count":19,"URL":"https:\/\/doi.org\/10.1109\/icip42928.2021.9506711","relation":{},"subject":[],"published":{"date-parts":[[2021,9,19]]}}}