{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T02:21:24Z","timestamp":1730254884620,"version":"3.28.0"},"reference-count":51,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"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":[[2021,1,10]]},"DOI":"10.1109\/icpr48806.2021.9412277","type":"proceedings-article","created":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T02:15:54Z","timestamp":1620267354000},"page":"9711-9718","source":"Crossref","is-referenced-by-count":1,"title":["Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification"],"prefix":"10.1109","author":[{"given":"Arslan","family":"Ali","sequence":"first","affiliation":[{"name":"Politecnico di Torino,Department of Electronics and Telecommunications,Turin,Italy"}]},{"given":"Andrea","family":"Migliorati","sequence":"additional","affiliation":[{"name":"Politecnico di Torino,Department of Electronics and Telecommunications,Turin,Italy"}]},{"given":"Tiziano","family":"Bianchi","sequence":"additional","affiliation":[{"name":"Politecnico di Torino,Department of Electronics and Telecommunications,Turin,Italy"}]},{"given":"Enrico","family":"Magli","sequence":"additional","affiliation":[{"name":"Politecnico di Torino,Department of Electronics and Telecommunications,Turin,Italy"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Adversarial autoencoders","author":"makhzani","year":"2015","journal-title":"ArXiv Preprint"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01258-8_32"},{"key":"ref33","article-title":"Are adversarial examples inevitable?","author":"shafahi","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2016.41"},{"key":"ref31","article-title":"Delving into transferable adversarial examples and black-box attacks","author":"liu","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.17"},{"key":"ref37","first-page":"2266","article-title":"Formal guarantees on the robustness of a classifier against adversarial manipulation","author":"hein","year":"2017","journal-title":"In Advances in Neural Information Processing Systems"},{"key":"ref36","article-title":"Adversarial patch","author":"brown","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978392"},{"key":"ref34","article-title":"Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients","author":"ross","year":"0","journal-title":"Thirty-Second AAAI Conference on Artificial Intelligence"},{"key":"ref28","article-title":"Learning with a strong adversary","author":"huang","year":"2015","journal-title":"ArXiv Preprint"},{"key":"ref27","article-title":"Explaining and harnessing adversarial examples","author":"goodfellow","year":"2014","journal-title":"ArXiv Preprint"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.282"},{"key":"ref2","first-page":"1058","article-title":"Regularization of neural networks using dropconnect","author":"wan","year":"0","journal-title":"In International Conference on Machine Learning"},{"key":"ref1","article-title":"Multi-column deep neural networks for image classification","author":"cire?an","year":"2012","journal-title":"ArXiv Preprint"},{"key":"ref20","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009","journal-title":"Technical Report Citeseer"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2016.36"},{"key":"ref21","article-title":"Towards deep learning models resistant to adversarial attacks","author":"madry","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014066"},{"key":"ref23","article-title":"Adversarial examples in the physical world","author":"kurakin","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref26","article-title":"Intriguing properties of neural networks","author":"szegedy","year":"2013","journal-title":"ArXiv Preprint"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/1081870.1081950"},{"key":"ref50","article-title":"Is pgd-adversarial training necessary? alternative training via a soft-quantization network with noisy-natural samples only","author":"zheng","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref51","article-title":"An empirical evaluation of adversarial robustness under transfer learning","author":"davchev","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref10","article-title":"Improved adversarial robustness via logit regularization methods","author":"summers","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00929"},{"key":"ref40","article-title":"Auto-encoding variational bayes","author":"kingma","year":"2013","journal-title":"ArXiv Preprint"},{"key":"ref12","first-page":"11190","article-title":"Unlabeled data improves adversarial robustness","author":"carmon","year":"2019","journal-title":"In Advances in Neural Information Processing Systems"},{"key":"ref13","first-page":"11838","article-title":"Theoretical evidence for adversarial robustness through randomization","author":"pinot","year":"2019","journal-title":"In Advances in Neural Information Processing Systems"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33012253"},{"key":"ref15","article-title":"On evaluating adversarial robustness","author":"carlini","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3128572.3140444"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref18","article-title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms","author":"xiao","year":"2017","journal-title":"ArXiv Preprint"},{"journal-title":"Reading digits in natural images with unsupervised feature learning","year":"2011","author":"netzer","key":"ref19"},{"key":"ref4","article-title":"Augmented cyclegan: Learning many-to-many mappings from unpaired data","author":"almahairi","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref3","first-page":"1988","article-title":"Deep learning face representation by joint identification-verification","author":"sun","year":"2014","journal-title":"In Advances in Neural Information Processing Systems"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00175"},{"key":"ref5","first-page":"1097","article-title":"Im-agenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"In Advances in Neural Information Processing Systems"},{"key":"ref8","article-title":"Classification regions of deep neural networks","author":"fawzi","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaw4399"},{"key":"ref49","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"The Journal of Machine Learning Research"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2740965"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9884.00122"},{"key":"ref48","article-title":"A closer look at deep learning heuristics: Learning rate restarts, warmup and distillation","author":"gotmare","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref47","article-title":"Shake-shake regularization","author":"gastaldi","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref42","article-title":"Deep linear discriminant analysis","author":"dorfer","year":"2015","journal-title":"ArXiv Preprint"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2012.2183645"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/MMSP.2019.8901698"},{"key":"ref43","article-title":"Discriminant analysis for dimensionality reduction: An overview of recent developments","author":"ye","year":"2010","journal-title":"Biometrics Theory Methods and Applications"}],"event":{"name":"2020 25th International Conference on Pattern Recognition (ICPR)","start":{"date-parts":[[2021,1,10]]},"location":"Milan, Italy","end":{"date-parts":[[2021,1,15]]}},"container-title":["2020 25th International Conference on Pattern Recognition (ICPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9411940\/9411911\/09412277.pdf?arnumber=9412277","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:09:04Z","timestamp":1659485344000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9412277\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,10]]},"references-count":51,"URL":"https:\/\/doi.org\/10.1109\/icpr48806.2021.9412277","relation":{},"subject":[],"published":{"date-parts":[[2021,1,10]]}}}