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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2022,9,30]]},"abstract":"<jats:p>Detecting out-of-distribution (OOD) inputs for deep learning models is a critical task when models are deployed in real-world environments. Recently, a large number of works have been dedicated to tackling the OOD detection problem. One of the most straightforward and effective ways is OOD training, which adds heterogeneous auxiliary data in the training stage. However, the extra auxiliary data cannot be involved arbitrarily. A high-quality and powerful auxiliary dataset must contain samples that belong to OOD but are close to in-distribution (ID), which can teach the model to learn more information about OOD samples, furthermore, distinguish OOD from ID. The key issue for this problem is how to simply acquire such distinctive OOD samples. In this article, we propose an enhanced Mixup-based OOD (MixOOD) detection strategy that can be attached to any threshold-based OOD detecting method. Different from the traditional Mixup designed for ID data augmentation, our proposed MixOOD generates augmented images with deliberately modified Mixup and then uses them as auxiliary OOD data to leverage the OOD detection. We test our method with classical OOD detecting approaches like Maximum Softmax Probability, Energy Score, and Out-of-distribution detector for Neural networks. Experiments show that models with MixOOD can better distinguish in- and out-of-distribution samples than the original version of each approach.<\/jats:p>","DOI":"10.1145\/3578935","type":"journal-article","created":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T12:59:05Z","timestamp":1672837145000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["MixOOD: Improving Out-of-distribution Detection with Enhanced Data Mixup"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3599-9961","authenticated-orcid":false,"given":"Taocun","family":"Yang","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Haidian, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4465-372X","authenticated-orcid":false,"given":"Yaping","family":"Huang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Haidian, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4592-5387","authenticated-orcid":false,"given":"Yanlin","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, Haidian, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1782-1051","authenticated-orcid":false,"given":"Junbo","family":"Liu","sequence":"additional","affiliation":[{"name":"Infrastructure Inspection Research Institute, China Academy of Railway Science Corporation Limited, Haidian, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0003-1002","authenticated-orcid":false,"given":"Shengchun","family":"Wang","sequence":"additional","affiliation":[{"name":"Infrastructure Inspection Research Institute, China Academy of Railway Science Corporation Limited, Haidian, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2023,3,16]]},"reference":[{"key":"e_1_3_1_2_2","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. 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