{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T07:04:25Z","timestamp":1763535865722,"version":"3.28.0"},"reference-count":43,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"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\/icpr48806.2021.9412616","type":"proceedings-article","created":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T02:15:54Z","timestamp":1620267354000},"page":"9172-9179","source":"Crossref","is-referenced-by-count":11,"title":["Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks"],"prefix":"10.1109","author":[{"given":"Denis","family":"Huseljic","sequence":"first","affiliation":[]},{"given":"Bernhard","family":"Sick","sequence":"additional","affiliation":[]},{"given":"Marek","family":"Herde","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Kottke","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"year":"2017","key":"ref39"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966217"},{"journal-title":"The MNIST Database of Handwritten Digits","year":"1998","author":"lecun","key":"ref33"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/0377-2217(82)90161-8"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref30","volume":"37","author":"mockus","year":"2012","journal-title":"Bayesian Approach to Global Optimization Theory and Applications"},{"journal-title":"Learning multiple layers of features from tiny images","year":"2009","author":"krizhevsky","key":"ref37"},{"key":"ref36","article-title":"Reading Digits in Natural Images with Unsupervised Feature Learning","author":"netzer","year":"0","journal-title":"NeurIPS workshop"},{"journal-title":"NOT-MNIST Data Set","year":"2011","author":"bulatov","key":"ref35"},{"journal-title":"kMNIST Data Set","year":"2018","author":"clanuwat","key":"ref34"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-0745-0"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref11","first-page":"1613","article-title":"Weight Uncertainty in Neural Networks","author":"blundell","year":"0","journal-title":"ICML"},{"journal-title":"Uncertainty in deep learning","year":"2016","author":"gal","key":"ref12"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1992.4.3.448"},{"journal-title":"Pattern Recognition and Machine Learning","year":"2006","author":"bishop","key":"ref14"},{"key":"ref15","first-page":"1050","article-title":"Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning","author":"gal","year":"0","journal-title":"ICML"},{"key":"ref16","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"JMLR"},{"key":"ref17","first-page":"13969","article-title":"Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Data set Shift","author":"ovadia","year":"0","journal-title":"NeurIPS"},{"key":"ref18","first-page":"3581","article-title":"Concrete Dropout","author":"gal","year":"0","journal-title":"NeurIPS"},{"key":"ref19","article-title":"Deep Ensembles: A Loss Landscape Perspective","author":"fort","year":"2019","journal-title":"ArXiv"},{"key":"ref28","first-page":"1321","article-title":"On Calibration of Modern Neural Networks","author":"guo","year":"0","journal-title":"ICML"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2018.2873900"},{"key":"ref27","article-title":"Adam: A Method for Stochastic Optimization","author":"kingma","year":"0","journal-title":"ICLRE"},{"key":"ref3","first-page":"5574","article-title":"What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?","author":"kendall","year":"0","journal-title":"NeurIPS"},{"key":"ref6","first-page":"6402","article-title":"Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles","author":"lakshminarayanan","year":"0","journal-title":"NeurIPS"},{"journal-title":"The Dirichlet-Multinomial and Dirichlet-Categorical models for Bayesian inference","year":"2014","author":"tu","key":"ref29"},{"key":"ref5","first-page":"1861","article-title":"Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks","author":"hernandez-lobato","year":"0","journal-title":"ICML"},{"key":"ref8","article-title":"Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples","author":"lee","year":"0","journal-title":"ICLRE"},{"key":"ref7","first-page":"14547","article-title":"Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness","author":"malinin","year":"0","journal-title":"NeurIPS"},{"key":"ref2","article-title":"Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference","author":"gal","year":"2016","journal-title":"ArXiv"},{"key":"ref9","first-page":"2672","article-title":"Generative Adversarial Nets","author":"goodfellow","year":"0","journal-title":"NeurIPS"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"journal-title":"Geometry and uncertainty in deep learning for computer vision","year":"2017","author":"kendall","key":"ref20"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.2172\/800792"},{"key":"ref21","first-page":"3179","article-title":"Evidential Deep Learning to Quantify Classification Uncertainty","author":"sensoy","year":"0","journal-title":"NeurIPS"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/SASOW.2014.25"},{"journal-title":"Information Theory and Statistics","year":"1997","author":"kullback","key":"ref24"},{"key":"ref41","article-title":"A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks","author":"hendrycks","year":"0","journal-title":"ICLRE"},{"key":"ref23","first-page":"7047","article-title":"Predictive Uncertainty Estimation via Prior Networks","author":"malinin","year":"0","journal-title":"NeurIPS"},{"key":"ref26","article-title":"Out-of-distribution Detection in Classifiers via Generation","author":"vernekar","year":"2019","journal-title":"ArXiv"},{"key":"ref43","article-title":"Toward optimal probabilistic active learning using a bayesian approach","author":"kottke","year":"2020","journal-title":"ArXiv"},{"key":"ref25","article-title":"Auto-Encoding Variational Bayes","author":"kingma","year":"0","journal-title":"ICLRE"}],"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\/09412616.pdf?arnumber=9412616","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:40:40Z","timestamp":1652197240000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9412616\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,10]]},"references-count":43,"URL":"https:\/\/doi.org\/10.1109\/icpr48806.2021.9412616","relation":{},"subject":[],"published":{"date-parts":[[2021,1,10]]}}}