{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T02:52:54Z","timestamp":1775098374343,"version":"3.50.1"},"reference-count":41,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"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":[[2021,6]]},"DOI":"10.1109\/cvpr46437.2021.00381","type":"proceedings-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T21:56:02Z","timestamp":1635890162000},"page":"3814-3823","source":"Crossref","is-referenced-by-count":6,"title":["Robust Bayesian Neural Networks by Spectral Expectation Bound Regularization"],"prefix":"10.1109","author":[{"given":"Jiaru","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Hua","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengui","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengyu","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruhui","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haibing","family":"Guan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33017322"},{"key":"ref38","article-title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms","author":"xiao","year":"2017"},{"key":"ref33","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"ICLRE"},{"key":"ref32","article-title":"The singular values of convolutional layers","author":"sedghi","year":"2018","journal-title":"ICLRE"},{"key":"ref31","article-title":"L2-nonexpansive neural networks","author":"qian","year":"2019","journal-title":"ICLRE"},{"key":"ref30","article-title":"Bayesian deep learning and uncertainty in computer vision","author":"phan","year":"2019","journal-title":"Master&#x2019;s thesis"},{"key":"ref37","article-title":"Evaluating the robustness of neural networks: An extreme value theory approach","author":"weng","year":"2018","journal-title":"ICLRE"},{"key":"ref36","article-title":"On the convergence and robustness of adversarial training","author":"wang","year":"2019","journal-title":"ICML"},{"key":"ref35","article-title":"Is robustness the cost of accuracy? - A comprehensive study on the robustness of 18 deep image classification models","author":"su","year":"2018","journal-title":"ECCV"},{"key":"ref34","article-title":"Understanding measures of uncertainty for adversarial example detection","author":"smith","year":"2018"},{"key":"ref10","article-title":"Parseval networks: Improving robustness to adversarial examples","author":"cisse","year":"2017","journal-title":"ICML"},{"key":"ref40","article-title":"Spectral norm regularization for improving the generalizability of deep learning","author":"yoshida","year":"2017"},{"key":"ref11","article-title":"Modeling Epistemic and Aleatoric Uncertainty with Bayesian Neural Networks and Latent Variables","author":"depeweg","year":"2019","journal-title":"PhD thesis"},{"key":"ref12","article-title":"Generalizable adversarial training via spectral normalization","author":"farnia","year":"2018","journal-title":"ICLRE"},{"key":"ref13","article-title":"Sufficient conditions for idealised models to have no adversarial examples: a theoretical and empirical study with bayesian neural networks","author":"gal","year":"2018"},{"key":"ref14","article-title":"Explaining and harnessing adversarial examples","author":"goodfellow","year":"2015","journal-title":"ICLRE"},{"key":"ref15","article-title":"Regularisation of neural networks by enforcing lips-chitz continuity","author":"gouk","year":"2018"},{"key":"ref16","article-title":"The limitations of model uncertainty in adversarial settings","author":"grosse","year":"2018"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00167"},{"key":"ref18","article-title":"Formal guarantees on the robustness of a classifier against adversarial manipulation","author":"hein","year":"2017","journal-title":"NeurIPS"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.425"},{"key":"ref28","article-title":"Spectral normalization for generative adversarial networks","author":"miyato","year":"2018","journal-title":"ICLRE"},{"key":"ref4","article-title":"Invertible residual networks","author":"behrmann","year":"2019","journal-title":"ICML"},{"key":"ref27","article-title":"Training-free uncertainty estimation for neural networks","author":"mi","year":"2019"},{"key":"ref3","article-title":"Spectrally-normalized margin bounds for neural networks","author":"bartlett","year":"2017","journal-title":"NeurIPS"},{"key":"ref6","author":"bietti","year":"2018"},{"key":"ref29","volume":"118","author":"neal","year":"2012","journal-title":"Bayesian learning for neural networks"},{"key":"ref5","article-title":"Bayesian adversarial spheres: Bayesian inference and adversarial examples in a noiseless setting","author":"bekasov","year":"2018"},{"key":"ref8","article-title":"Weight uncertainty in neural network","author":"blundell","year":"2015","journal-title":"ICML"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1285773"},{"key":"ref2","article-title":"Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples","author":"athalye","year":"2018","journal-title":"ICML"},{"key":"ref9","article-title":"Robustness of bayesian neural networks to gradient-based attacks","author":"carbone","year":"2020"},{"key":"ref1","article-title":"Understanding uncertainty in bayesian neural networks","author":"antor\u00e1n","year":"2019","journal-title":"Master&#x2019;s thesis"},{"key":"ref20","article-title":"What uncertainties do we need in bayesian deep learning for computer vision?","author":"kendall","year":"2017","journal-title":"NeurIPS"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref21","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref24","article-title":"Dropout inference in bayesian neural networks with alpha-divergences","author":"li","year":"2017","journal-title":"ICML"},{"key":"ref41","article-title":"Theoretically principled trade-off between robustness and accuracy","author":"zhang","year":"2019","journal-title":"ICML"},{"key":"ref23","article-title":"Preventing gradient attenuation in lipschitz constrained convolutional networks","author":"li","year":"2019","journal-title":"NeurIPS"},{"key":"ref26","article-title":"Towards deep learning models resistant to adversarial attacks","author":"madry","year":"2018","journal-title":"ICLRE"},{"key":"ref25","article-title":"Adv-BNN: Improved adversarial defense through robust bayesian neural network","author":"liu","year":"2019","journal-title":"ICLRE"}],"event":{"name":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","location":"Nashville, TN, USA","start":{"date-parts":[[2021,6,20]]},"end":{"date-parts":[[2021,6,25]]}},"container-title":["2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9577055\/9577056\/09577878.pdf?arnumber=9577878","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:47:57Z","timestamp":1652197677000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9577878\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6]]},"references-count":41,"URL":"https:\/\/doi.org\/10.1109\/cvpr46437.2021.00381","relation":{},"subject":[],"published":{"date-parts":[[2021,6]]}}}