{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T18:10:13Z","timestamp":1756491013162,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":31,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T00:00:00Z","timestamp":1703203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"The National Natural Science Foundation of China,","award":["62206054"],"award-info":[{"award-number":["62206054"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,12,22]]},"DOI":"10.1145\/3639631.3639673","type":"proceedings-article","created":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T06:08:33Z","timestamp":1708063713000},"page":"248-253","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Outlier Exposure with Focal Loss for Out-of-distribution Detection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2140-9042","authenticated-orcid":false,"given":"Qichao","family":"Chen","sequence":"first","affiliation":[{"name":"University of Nottingham Malaysia, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4915-1593","authenticated-orcid":false,"given":"Zhiyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Nottingham Malaysia, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3563-1115","authenticated-orcid":false,"given":"Tomas","family":"Maul","sequence":"additional","affiliation":[{"name":"University of Nottingham Malaysia, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3272-4084","authenticated-orcid":false,"given":"Kuan","family":"Li","sequence":"additional","affiliation":[{"name":"Dongguan University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5474-4764","authenticated-orcid":false,"given":"Jianping","family":"Yin","sequence":"additional","affiliation":[{"name":"Dongguan University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,2,16]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Data augmentation by autoencoders for unsupervised anomaly detection. arXiv preprint arXiv:1912.13384","author":"Babaei Kasra","year":"2019","unstructured":"Kasra Babaei, ZhiYuan Chen, and Tomas Maul. 2019. Data augmentation by autoencoders for unsupervised anomaly detection. arXiv preprint arXiv:1912.13384 (2019)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Mu Cai and Yixuan Li. 2023. Out-of-distribution Detection via Frequency-regularized Generative Models. In WACV. 5521\u20135530.","DOI":"10.1109\/WACV56688.2023.00548"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Qichao Chen Wenjie Jiang Kuan Li and Yi Wang. 2022. Improving Energy-Based Out-of-Distribution Detection by Sparsity Regularization. In PAKDD. 539\u2013551.","DOI":"10.1007\/978-3-031-05936-0_42"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Mircea Cimpoi Subhransu Maji Iasonas Kokkinos Sammy Mohamed and Andrea Vedaldi. 2014. Describing textures in the wild. In CVPR. 3606\u20133613.","DOI":"10.1109\/CVPR.2014.461"},{"key":"e_1_3_2_1_5_1","unstructured":"Zhen Fang Yixuan Li Jie Lu Jiahua Dong Bo Han and Feng Liu. 2022. Is Out-of-Distribution Detection Learnable?. In NeurIPS Vol.\u00a035. 37199\u201337213."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Ross Girshick Jeff Donahue Trevor Darrell and Jitendra Malik. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR. 580\u2013587.","DOI":"10.1109\/CVPR.2014.81"},{"key":"e_1_3_2_1_7_1","unstructured":"Jakob\u00a0D Havtorn Jes Frellsen S\u00f8ren Hauberg and Lars Maal\u00f8e. 2021. Hierarchical vaes know what they don\u2019t know. In ICML. 4117\u20134128."},{"key":"e_1_3_2_1_8_1","volume-title":"Scaling out-of-distribution detection for real-world settings. arXiv preprint arXiv:1911.11132","author":"Hendrycks Dan","year":"2019","unstructured":"Dan Hendrycks, Steven Basart, Mantas Mazeika, Andy Zou, Joe Kwon, Mohammadreza Mostajabi, Jacob Steinhardt, and Dawn Song. 2019. Scaling out-of-distribution detection for real-world settings. arXiv preprint arXiv:1911.11132 (2019)."},{"key":"e_1_3_2_1_9_1","unstructured":"Dan Hendrycks and Kevin Gimpel. 2017. A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks. In ICLR."},{"key":"e_1_3_2_1_10_1","unstructured":"Dan Hendrycks Mantas Mazeika and Thomas Dietterich. 2019. Deep Anomaly Detection with Outlier Exposure. In ICLR."},{"key":"e_1_3_2_1_11_1","unstructured":"Yen-Chang Hsu Yilin Shen Hongxia Jin and Zsolt Kira. 2020. Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data. In CVPR. 10951\u201310960."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Gao Huang Zhuang Liu Laurens Van\u00a0Maaten and Kilian Weinberger. 2017. Densely connected convolutional networks. In CVPR. 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_3_2_1_13_1","unstructured":"Alex Krizhevsky Geoffrey Hinton 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_14_1","volume-title":"Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey\u00a0E Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012)."},{"key":"e_1_3_2_1_15_1","unstructured":"Shiyu Liang Yixuan Li and Rayadurgam Srikant. 2017. Enhancing the reliability of out-of-distribution image detection in neural networks. In ICLR."},{"key":"e_1_3_2_1_16_1","unstructured":"Tsung-Yi Lin Priya Goyal Ross Girshick Kaiming He and Piotr Doll\u00e1r. 2017. Focal loss for dense object detection. In ICCV. 2980\u20132988."},{"key":"e_1_3_2_1_17_1","unstructured":"Weitang Liu Xiaoyun Wang John\u00a0D Owens and Yixuan Li. 2020. Energy-based out-of-distribution detection. In NeurIPS Vol.\u00a033. 21464\u201321475."},{"key":"e_1_3_2_1_18_1","volume-title":"NIPS Workshop on Deep Learning and Unsupervised Feature Learning.","author":"Netzer Yuval","year":"2011","unstructured":"Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew\u00a0Y Ng. 2011. Reading digits in natural images with unsupervised feature learning. In NIPS Workshop on Deep Learning and Unsupervised Feature Learning."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Karsten Roth Latha Pemula Joaquin Zepeda Bernhard Sch\u00f6lkopf Thomas Brox and Peter Gehler. 2022. Towards total recall in industrial anomaly detection. In CVPR. 14318\u201314328.","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"e_1_3_2_1_20_1","unstructured":"Chandramouli\u00a0Shama Sastry and Sageev Oore. 2020. Detecting out-of-distribution examples with gram matrices. In ICML. 8491\u20138501."},{"key":"e_1_3_2_1_21_1","volume-title":"DICE: Leveraging Sparsification for Out-of-Distribution Detection. In ECCV.","author":"Sun Yiyou","year":"2022","unstructured":"Yiyou Sun and Yixuan Li. 2022. DICE: Leveraging Sparsification for Out-of-Distribution Detection. In ECCV."},{"key":"e_1_3_2_1_22_1","unstructured":"Jost Tobias Alexey Dosovitskiy Thomas Brox and Martin\u00a0A. Riedmiller. 2015. Striving for Simplicity: The All Convolutional Net. In ICLR."},{"key":"e_1_3_2_1_23_1","volume-title":"Vim: Out-of-distribution with virtual-logit matching. In CVPR. 4921\u20134930.","author":"Wang Haoqi","year":"2022","unstructured":"Haoqi Wang, Zhizhong Li, Litong Feng, and Wayne Zhang. 2022. Vim: Out-of-distribution with virtual-logit matching. In CVPR. 4921\u20134930."},{"key":"e_1_3_2_1_24_1","first-page":"15545","article-title":"Watermarking for Out-of-distribution Detection","volume":"35","author":"Wang Qizhou","year":"2022","unstructured":"Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, and Bo Han. 2022. Watermarking for Out-of-distribution Detection. NeurIPS 35 (2022), 15545\u201315557.","journal-title":"NeurIPS"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3188763"},{"key":"e_1_3_2_1_26_1","first-page":"32598","article-title":"Openood: Benchmarking generalized out-of-distribution detection","volume":"35","author":"Yang Jingkang","year":"2022","unstructured":"Jingkang Yang, Pengyun Wang, Dejian Zou, Zitang Zhou, Kunyuan Ding, Wenxuan Peng, Haoqi Wang, Guangyao Chen, Bo Li, Yiyou Sun, 2022. Openood: Benchmarking generalized out-of-distribution detection. NeruIPS 35 (2022), 32598\u201332611.","journal-title":"NeruIPS"},{"key":"e_1_3_2_1_27_1","volume-title":"LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop. arXiv preprint arXiv:1506.03365","author":"Yu Fisher","year":"2015","unstructured":"Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao. 2015. LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop. arXiv preprint arXiv:1506.03365 (2015)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Sergey Zagoruyko and Nikos Komodakis. 2016. Wide Residual Networks. In BMVC.","DOI":"10.5244\/C.30.87"},{"key":"e_1_3_2_1_29_1","volume-title":"OpenOOD v1. 5: Enhanced Benchmark for Out-of-Distribution Detection. arXiv preprint arXiv:2306.09301","author":"Zhang Jingyang","year":"2023","unstructured":"Jingyang Zhang, Jingkang Yang, Pengyun Wang, Haoqi Wang, Yueqian Lin, Haoran Zhang, Yiyou Sun, Xuefeng Du, Kaiyang Zhou, Wayne Zhang, 2023. OpenOOD v1. 5: Enhanced Benchmark for Out-of-Distribution Detection. arXiv preprint arXiv:2306.09301 (2023)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2723009"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Yibo Zhou. 2022. Rethinking reconstruction autoencoder-based out-of-distribution detection. In CVPR. 7379\u20137387.","DOI":"10.1109\/CVPR52688.2022.00723"}],"event":{"name":"ACAI 2023: 2023 6th International Conference on Algorithms, Computing and Artificial Intelligence","acronym":"ACAI 2023","location":"Sanya China"},"container-title":["2023 6th International Conference on Algorithms Computing and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3639631.3639673","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3639631.3639673","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T17:41:13Z","timestamp":1756489273000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3639631.3639673"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,22]]},"references-count":31,"alternative-id":["10.1145\/3639631.3639673","10.1145\/3639631"],"URL":"https:\/\/doi.org\/10.1145\/3639631.3639673","relation":{},"subject":[],"published":{"date-parts":[[2023,12,22]]},"assertion":[{"value":"2024-02-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}