{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T02:24:37Z","timestamp":1730255077171,"version":"3.28.0"},"reference-count":42,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T00:00:00Z","timestamp":1661040000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T00:00:00Z","timestamp":1661040000000},"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":[[2022,8,21]]},"DOI":"10.1109\/icpr56361.2022.9956495","type":"proceedings-article","created":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T14:34:13Z","timestamp":1669732453000},"page":"2643-2649","source":"Crossref","is-referenced-by-count":1,"title":["Effortless Training of Joint Energy-Based Models with Sliced Score Matching"],"prefix":"10.1109","author":[{"given":"Xixi","family":"Liu","sequence":"first","affiliation":[{"name":"Chalmers University of Technology,Department of Electrical Engineering,Gothenburg,Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D.","family":"Staudt","sequence":"additional","affiliation":[{"name":"Chalmers University of Technology,Department of Electrical Engineering,Gothenburg,Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Che-Tsung","family":"Lin","sequence":"additional","affiliation":[{"name":"Chalmers University of Technology,Department of Electrical Engineering,Gothenburg,Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher","family":"Zach","sequence":"additional","affiliation":[{"name":"Chalmers University of Technology,Department of Electrical Engineering,Gothenburg,Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"krizhevsky","key":"ref39"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"journal-title":"A Tutorial on Energy-based Learning","year":"2006","author":"lecun","key":"ref33"},{"key":"ref32","first-page":"61","article-title":"Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods","volume":"10","author":"platt","year":"1999","journal-title":"Advances in Large Margin Classifiers"},{"key":"ref31","article-title":"Obtaining well calibrated probabilities using bayesian binning","author":"naeini","year":"2015","journal-title":"AAAI"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/775047.775151"},{"key":"ref37","article-title":"Wide residual networks","author":"zagoruyko","year":"2016","journal-title":"BMVC"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1009213728"},{"article-title":"Detecting out-of-distribution inputs to deep generative models using typicality","year":"2019","author":"nalisnick","key":"ref35"},{"key":"ref34","article-title":"Gated softmax classification","author":"memisevic","year":"2010","journal-title":"NeurIPS"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000001437"},{"key":"ref40","article-title":"Reading digits in natural images with unsupervised feature learning","author":"netzer","year":"2011","journal-title":"NIPS"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1162\/NECO_a_00142"},{"key":"ref12","first-page":"574","article-title":"Sliced score matching: A scalable approach to density and score estimation","author":"song","year":"2020","journal-title":"Uncertainty in Artificial Intelligence"},{"article-title":"Deep learning for anomaly detection: A survey","year":"2019","author":"chalapathy","key":"ref13"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3152527"},{"article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","year":"2016","author":"hendrycks","key":"ref15"},{"article-title":"Enhancing the reliability of out-of-distribution image detection in neural networks","year":"2017","author":"liang","key":"ref16"},{"article-title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks","year":"2018","author":"lee","key":"ref17"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01096"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007665907178"},{"article-title":"Do deep generative models know what they don’t know?","year":"2018","author":"nalisnick","key":"ref28"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1162\/089976602760128018"},{"article-title":"Likelihood regret: An out-of-distribution detection score for variational auto-encoder","year":"2020","author":"xiao","key":"ref27"},{"article-title":"Your classifier is secretly an energy based model and you should treat it like one","year":"2019","author":"grathwohl","key":"ref3"},{"key":"ref6","article-title":"Bayesian learning via stochastic gradient langevin dynamics","author":"welling","year":"2011","journal-title":"ICML"},{"key":"ref29","article-title":"Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers","author":"zadrozny","year":"2001","journal-title":"ICML"},{"key":"ref5","first-page":"33","article-title":"On contrastive divergence learning","volume":"10","author":"carreira-perpinan","year":"2005","journal-title":"AISTATS"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1214\/12-AOS971"},{"key":"ref7","first-page":"695","article-title":"Estimation of non-normalized statistical models by score matching","volume":"6","author":"hyvarinen","year":"2005","journal-title":"Journal of Machine Learning Research"},{"key":"ref2","article-title":"On calibration of modern neural networks","author":"guo","year":"2017","journal-title":"ICML"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s40300-014-0039-y"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298640"},{"article-title":"Auto-encoding variational bayes","year":"2013","author":"kingma","key":"ref20"},{"key":"ref22","article-title":"Variational inference with normalizing flows","author":"rezende","year":"2015","journal-title":"ICML"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1285773"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2004.1315150"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01401"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"article-title":"Density estimation using real nvp","year":"2016","author":"dinh","key":"ref23"},{"article-title":"Improving reconstruction autoencoder out-of-distribution detection with mahalanobis distance","year":"2018","author":"denouden","key":"ref26"},{"key":"ref25","article-title":"Implicit generation and modeling with energy based models","author":"du","year":"2019","journal-title":"NeurIPS"}],"event":{"name":"2022 26th International Conference on Pattern Recognition (ICPR)","start":{"date-parts":[[2022,8,21]]},"location":"Montreal, QC, Canada","end":{"date-parts":[[2022,8,25]]}},"container-title":["2022 26th International Conference on Pattern Recognition (ICPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9956007\/9955631\/09956495.pdf?arnumber=9956495","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T15:03:42Z","timestamp":1671462222000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9956495\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,21]]},"references-count":42,"URL":"https:\/\/doi.org\/10.1109\/icpr56361.2022.9956495","relation":{},"subject":[],"published":{"date-parts":[[2022,8,21]]}}}