{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:16:38Z","timestamp":1766067398309,"version":"3.28.0"},"reference-count":29,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"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":[[2021,7,18]]},"DOI":"10.1109\/ijcnn52387.2021.9533465","type":"proceedings-article","created":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T21:27:41Z","timestamp":1632173261000},"page":"1-8","source":"Crossref","is-referenced-by-count":5,"title":["FOOD: Fast Out-Of-Distribution Detector"],"prefix":"10.1109","author":[{"given":"Guy","family":"Amit","sequence":"first","affiliation":[]},{"given":"Moshe","family":"Levy","sequence":"additional","affiliation":[]},{"given":"Ishai","family":"Rosenberg","sequence":"additional","affiliation":[]},{"given":"Asaf","family":"Shabtai","sequence":"additional","affiliation":[]},{"given":"Yuval","family":"Elovici","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","article-title":"Detecting out-of-distribution examples with in-distribution examples and gram matrices","author":"sastry","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00950"},{"journal-title":"Reading digits in natural images with unsupervised feature learning","year":"2011","author":"netzer","key":"ref12"},{"journal-title":"Learning multiple layers of features from tiny images","year":"2009","author":"krizhevsky","key":"ref13"},{"key":"ref14","first-page":"3518","article-title":"Factoring variations in natural images with deep gaussian mixture models","author":"van den oord","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2017-830"},{"journal-title":"An Introduction to Multivariate Statistical Analysis","year":"1958","author":"anderson","key":"ref16"},{"key":"ref17","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","author":"hendrycks","year":"2017","journal-title":"5th International Conference on Learning Representations ICLR 2017"},{"key":"ref18","first-page":"7167","article-title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks","author":"lee","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref19","article-title":"Enhancing the reliability of out-of-distribution image detection in neural networks","author":"liang","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref4","article-title":"Concrete problems in ai safety","author":"amodei","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref27","article-title":"Preventing clean label poisoning using gaussian mixture loss","author":"yaseen","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5966"},{"key":"ref29","article-title":"Measuring dataset granularity","author":"cui","year":"2019","journal-title":"CoRR vol abs\/1912 10154"},{"key":"ref5","article-title":"Practical solutions for machine learning safety in autonomous vehicles","author":"mohseni","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref8","article-title":"A less biased evaluation of ood sample detectors","author":"shafaei","year":"0","journal-title":"Proceedings of the British Machine Vision Conference (BMVC)"},{"key":"ref7","article-title":"Deep anomaly detection with outlier exposure","author":"hendrycks","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref2","volume":"abs 1604 7316","author":"bojarski","year":"2016","journal-title":"End to End Learning for Self-Driving Cars"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01096"},{"key":"ref1","first-page":"2048","article-title":"Show, attend and tell: Neural image caption generation with visual attention","author":"xu","year":"0","journal-title":"International Conference on Machine Learning"},{"key":"ref20","article-title":"Robust out-of-distribution detection in neural networks","author":"chen","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref22","article-title":"Explaining and harnessing adversarial examples","author":"goodfellow","year":"2015","journal-title":"CoRR vol abs\/1412 6572"},{"key":"ref21","article-title":"Intriguing properties of neural networks","author":"szegedy","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3128572.3140444"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8852285"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2807385"}],"event":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2021,7,18]]},"location":"Shenzhen, China","end":{"date-parts":[[2021,7,22]]}},"container-title":["2021 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9533266\/9533267\/09533465.pdf?arnumber=9533465","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:45:54Z","timestamp":1652197554000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9533465\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,18]]},"references-count":29,"URL":"https:\/\/doi.org\/10.1109\/ijcnn52387.2021.9533465","relation":{},"subject":[],"published":{"date-parts":[[2021,7,18]]}}}