{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T16:40:15Z","timestamp":1786120815966,"version":"build-2736575974"},"publisher-location":"New York, NY, USA","reference-count":96,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T00:00:00Z","timestamp":1786147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Australian Research Council","award":["DP22010252"],"award-info":[{"award-number":["DP22010252"]}]},{"name":"Office of National Intelligence","award":["NI240100159"],"award-info":[{"award-number":["NI240100159"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,9]]},"DOI":"10.1145\/3770855.3816455","type":"proceedings-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T17:15:37Z","timestamp":1786036537000},"page":"13360-13364","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["When Models Leave the Training Distribution: A Tutorial on OOD Detection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5485-5595","authenticated-orcid":false,"given":"Suranga","family":"Seneviratne","sequence":"first","affiliation":[{"name":"The University of Sydney, Darlington, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8717-098X","authenticated-orcid":false,"given":"Dishanika","family":"Denipitiyage","sequence":"additional","affiliation":[{"name":"The University of Sydney, Darlington, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8102-2572","authenticated-orcid":false,"given":"Sanjay","family":"Chawla","sequence":"additional","affiliation":[{"name":"Qatar Computing Research Institute, HBKU, Doha, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1696-0156","authenticated-orcid":false,"given":"Aditya","family":"Menon","sequence":"additional","affiliation":[{"name":"Google, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,8,8]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW60793.2023.00369"},{"key":"e_1_3_2_1_2_1","volume-title":"Concrete problems in AI safety. arXiv:1606.06565","author":"Amodei Dario","year":"2016","unstructured":"Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Man\u00e9. 2016. Concrete problems in AI safety. arXiv:1606.06565 (2016)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.52202\/085713-4245"},{"key":"e_1_3_2_1_4_1","first-page":"91621","article-title":"Double Descent Meets Out-of-Distribution Detection: Theoretical Insights and Empirical Analysis on the Role of Model Complexity","volume":"38","author":"Ammar Mou\u00efn Ben","year":"2025","unstructured":"Mou\u00efn Ben Ammar, David Brellmann, Arturo Mendoza, Antoine Manzanera, and Gianni Franchi. 2025. Double Descent Meets Out-of-Distribution Detection: Theoretical Insights and Empirical Analysis on the Role of Model Complexity. Advances in Neural Information Processing Systems, Vol. 38 (2025), 91621-91666.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_5_1","unstructured":"Julian Bitterwolf Maximilian M\u00fcller and Matthias Hein. 2023. In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation. arXiv preprint arXiv:2306.00826 (2023)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49357.2023.10094846"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2023.3264783"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.461"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2023.109991"},{"key":"e_1_3_2_1_10_1","first-page":"93440","article-title":"Out-of-Distribution Detection with Relative Angles","volume":"38","author":"Demirel Berker","year":"2025","unstructured":"Berker Demirel, Marco Fumero, and Francesco Locatello. 2025. Out-of-Distribution Detection with Relative Angles. Advances in Neural Information Processing Systems, Vol. 38 (2025), 93440-93470.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"key":"e_1_3_2_1_12_1","volume-title":"RankOOD - Class Ranking-based Out-of-Distribution Detection. In 2026 IEEE\/CVF Conference on Computer Vision and Pattern Recognition.","author":"Denipitiyage Dishanika","year":"2026","unstructured":"Dishanika Denipitiyage, Naveen Karunanayake, Suranga Seneviratne, and Sanjay Chawla. 2026. RankOOD - Class Ranking-based Out-of-Distribution Detection. In 2026 IEEE\/CVF Conference on Computer Vision and Pattern Recognition."},{"key":"e_1_3_2_1_13_1","volume-title":"Learning confidence for out-of-distribution detection in neural networks. preprint arXiv:1802.04865","author":"DeVries Terrance","year":"2018","unstructured":"Terrance DeVries and Graham W Taylor. 2018. Learning confidence for out-of-distribution detection in neural networks. preprint arXiv:1802.04865 (2018)."},{"key":"e_1_3_2_1_14_1","volume-title":"Advances in Neural Information Processing Systems","volume":"31","author":"Dhamija Akshay Raj","year":"2018","unstructured":"Akshay Raj Dhamija, Manuel G\u00fcnther, and Terrance Boult. 2018. Reducing network agnostophobia. Advances in Neural Information Processing Systems, Vol. 31 (2018)."},{"key":"e_1_3_2_1_15_1","volume-title":"Extremely simple activation shaping for out-of-distribution detection. arXiv preprint arXiv:2209.09858","author":"Djurisic Andrija","year":"2022","unstructured":"Andrija Djurisic, Nebojsa Bozanic, Arjun Ashok, and Rosanne Liu. 2022. Extremely simple activation shaping for out-of-distribution detection. arXiv preprint arXiv:2209.09858 (2022)."},{"key":"e_1_3_2_1_16_1","volume-title":"VOS: Learning what you don't know by virtual outlier synthesis. arXiv preprint arXiv:2202.01197","author":"Du Xuefeng","year":"2022","unstructured":"Xuefeng Du, Zhaoning Wang, Mu Cai, and Yixuan Li. 2022. VOS: Learning what you don't know by virtual outlier synthesis. arXiv preprint arXiv:2202.01197 (2022)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20610"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.52202\/079017-4268"},{"key":"e_1_3_2_1_19_1","volume-title":"International Conference on Machine Learning. PMLR, 3145-3153","author":"Filos Angelos","year":"2020","unstructured":"Angelos Filos, Panagiotis Tigkas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, and Yarin Gal. 2020. Can autonomous vehicles identify, recover from, and adapt to distribution shifts?. In International Conference on Machine Learning. PMLR, 3145-3153."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV61041.2025.00522"},{"key":"e_1_3_2_1_21_1","first-page":"12325","article-title":"GOOD: Training-free guided diffusion sampling for out-of-distribution detection","volume":"38","author":"Gao Xin","year":"2025","unstructured":"Xin Gao, Jiyao Liu, Guanghao Li, Yueming Lyu, Jianxiong Gao, Weichen Yu, Ningsheng Xu, Liang Wang, Caifeng Shan, Ziwei Liu, et al., 2025. GOOD: Training-free guided diffusion sampling for out-of-distribution detection. Advances in Neural Information Processing Systems, Vol. 38 (2025), 12325-12359.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2026.fever-1.4"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3674213.3674217"},{"key":"e_1_3_2_1_24_1","volume-title":"Trafficllm: LLMs for improved open-set encrypted traffic analysis. Computer Networks","author":"Ginige Yasod","year":"2025","unstructured":"Yasod Ginige, Bhanuka Silva, Thilini Dahanayaka, and Suranga Seneviratne. 2025. Trafficllm: LLMs for improved open-set encrypted traffic analysis. Computer Networks (2025), 111847."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3770854.3780237"},{"key":"e_1_3_2_1_26_1","first-page":"40717","article-title":"Refining norms: A post-hoc framework for ood detection in graph neural networks","volume":"38","author":"Gu Jiawei","year":"2025","unstructured":"Jiawei Gu, Ziyue Qiao, and Zechao Li. 2025. Refining norms: A post-hoc framework for ood detection in graph neural networks. Advances in Neural Information Processing Systems, Vol. 38 (2025), 40717-40755.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.85"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.184"},{"key":"e_1_3_2_1_29_1","volume-title":"Scaling out-of-distribution detection for real-world settings. 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. preprint arXiv:1911.11132 (2019)."},{"key":"e_1_3_2_1_30_1","volume-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv:1610.02136","author":"Hendrycks Dan","year":"2016","unstructured":"Dan Hendrycks and Kevin Gimpel. 2016. A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv:1610.02136 (2016)."},{"key":"e_1_3_2_1_31_1","volume-title":"Deep anomaly detection with outlier exposure. arXiv preprint arXiv:1812.04606","author":"Hendrycks Dan","year":"2018","unstructured":"Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich. 2018. Deep anomaly detection with outlier exposure. arXiv preprint arXiv:1812.04606 (2018)."},{"key":"e_1_3_2_1_32_1","volume-title":"Weidong Wang, Jialong Xu, Xiaoqi Yang, et al.","author":"Hong Zesheng","year":"2024","unstructured":"Zesheng Hong, Yubiao Yue, Yubin Chen, Lele Cong, Huanjie Lin, Yuanmei Luo, Mini Han Wang, Weidong Wang, Jialong Xu, Xiaoqi Yang, et al., 2024. Out-of-distribution detection in medical image analysis: A survey. arXiv preprint arXiv:2404.18279 (2024)."},{"key":"e_1_3_2_1_33_1","first-page":"119899","article-title":"Redundancy-Aware Test-Time Graph Out-of-Distribution Detection","volume":"38","author":"Hou Yue","year":"2025","unstructured":"Yue Hou, He Zhu, Ruomei Liu, Yingke Su, Junran Wu, and Ke Xu. 2025. Redundancy-Aware Test-Time Graph Out-of-Distribution Detection. Advances in Neural Information Processing Systems, Vol. 38 (2025), 119899-119942.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01096"},{"key":"e_1_3_2_1_35_1","first-page":"677","article-title":"On the importance of gradients for detecting distributional shifts in the wild","volume":"34","author":"Huang Rui","year":"2021","unstructured":"Rui Huang, Andrew Geng, and Yixuan Li. 2021. On the importance of gradients for detecting distributional shifts in the wild. Advances in Neural Information Processing Systems, Vol. 34 (2021), 677-689.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.52202\/085713-1797"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2022.05.011"},{"key":"e_1_3_2_1_38_1","volume-title":"Negative Label Guided OOD Detection with Pretrained Vision-Language Models. In The Twelfth International Conference on Learning Representations.","author":"Jiang Xue","unstructured":"Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, and Bo Han. [n.d.]. Negative Label Guided OOD Detection with Pretrained Vision-Language Models. In The Twelfth International Conference on Learning Representations."},{"key":"e_1_3_2_1_39_1","volume-title":"CRAFT: Class Ranking Aware Fine-Tuning for Enhanced Out-of-Distribution Detection. In 2025 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 4119-4128","author":"Karunanayake Naveen","year":"2025","unstructured":"Naveen Karunanayake, Suranga Seneviratne, and Sanjay Chawla. 2025a. CRAFT: Class Ranking Aware Fine-Tuning for Enhanced Out-of-Distribution Detection. In 2025 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 4119-4128."},{"key":"e_1_3_2_1_40_1","volume-title":"ExCeL: Combined Extreme and Collective Logit Information for Out-of-Distribution Detection. Transactions on Machine Learning Research","author":"Karunanayake Naveen","year":"2025","unstructured":"Naveen Karunanayake, Suranga Seneviratne, and Sanjay Chawla. 2025b. ExCeL: Combined Extreme and Collective Logit Information for Out-of-Distribution Detection. Transactions on Machine Learning Research (2025)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448115"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3395351.3401700"},{"key":"e_1_3_2_1_43_1","volume-title":"A survey on out-of-distribution detection in NLP. arXiv preprint arXiv:2305.03236","author":"Lang Hao","year":"2023","unstructured":"Hao Lang, Yinhe Zheng, Yixuan Li, Jian Sun, Fei Huang, and Yongbin Li. 2023. A survey on out-of-distribution detection in NLP. arXiv preprint arXiv:2305.03236 (2023)."},{"key":"e_1_3_2_1_44_1","first-page":"154226","article-title":"Dualcnst: Enhancing zero-shot out-of-distribution detection via text-image consistency in vision-language models","volume":"38","author":"Le Fayi","year":"2025","unstructured":"Fayi Le, Wenwu He, Chentao Cao, Dong Liang, and Zhuo-Xu Cui. 2025. Dualcnst: Enhancing zero-shot out-of-distribution detection via text-image consistency in vision-language models. Advances in Neural Information Processing Systems, Vol. 38 (2025), 154226-154265.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_45_1","volume-title":"Tiny imagenet visual recognition challenge. CS 231N","author":"Le Ya","year":"2015","unstructured":"Ya Le and Xuan Yang. 2015. Tiny imagenet visual recognition challenge. CS 231N, Vol. 7, 7 (2015), 3."},{"key":"e_1_3_2_1_46_1","volume-title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks. Advances in neural information processing systems","author":"Lee Kimin","year":"2018","unstructured":"Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin. 2018. A simple unified framework for detecting out-of-distribution samples and adversarial attacks. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_1_47_1","volume-title":"Open set face recognition using transduction","author":"Li Fayin","year":"2005","unstructured":"Fayin Li and Harry Wechsler. 2005. Open set face recognition using transduction. IEEE transactions on pattern analysis and machine intelligence, Vol. 27, 11 (2005)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-024-02181-w"},{"key":"e_1_3_2_1_49_1","volume-title":"Revisiting Logit Distributions for Reliable Out-of-Distribution Detection. arXiv preprint arXiv:2510.20134","author":"Liang Jiachen","year":"2025","unstructured":"Jiachen Liang, Ruibing Hou, Minyang Hu, Hong Chang, Shiguang Shan, and Xilin Chen. 2025b. Revisiting Logit Distributions for Reliable Out-of-Distribution Detection. arXiv preprint arXiv:2510.20134 (2025)."},{"key":"e_1_3_2_1_50_1","volume-title":"Enhancing the reliability of out-of-distribution image detection in neural networks. arXiv preprint arXiv:1706.02690","author":"Liang Shiyu","year":"2017","unstructured":"Shiyu Liang, Yixuan Li, and Rayadurgam Srikant. 2017. Enhancing the reliability of out-of-distribution image detection in neural networks. arXiv preprint arXiv:1706.02690 (2017)."},{"key":"e_1_3_2_1_51_1","volume-title":"From out-of-distribution detection to hallucination detection: A geometric view. arXiv preprint arXiv:2602.07253","author":"Liu Litian","year":"2026","unstructured":"Litian Liu, Reza Pourreza, Yubing Jian, Yao Qin, and Roland Memisevic. 2026. From out-of-distribution detection to hallucination detection: A geometric view. arXiv preprint arXiv:2602.07253 (2026)."},{"key":"e_1_3_2_1_52_1","first-page":"120512","article-title":"Extremely simple multimodal outlier synthesis for out-of-distribution detection and segmentation","volume":"38","author":"Liu Moru","year":"2025","unstructured":"Moru Liu, Hao Dong, Jessica Kelly, Olga Fink, and Mario Trapp. 2025. Extremely simple multimodal outlier synthesis for out-of-distribution detection and segmentation. Advances in Neural Information Processing Systems, Vol. 38 (2025), 120512-120540.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_53_1","volume-title":"Energy-based out-of-distribution detection. Advances in neural information processing systems","author":"Liu Weitang","year":"2020","unstructured":"Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li. 2020. Energy-based out-of-distribution detection. Advances in neural information processing systems, Vol. 33 (2020), 21464-21475."},{"key":"e_1_3_2_1_54_1","first-page":"110728","article-title":"Mysteries of the Deep: Role of Intermediate Representations in Out of Distribution Detection","volume":"38","author":"De la Jara Ignacio Meza","year":"2025","unstructured":"Ignacio Meza De la Jara, Cristian Rodriguez-Opazo, Damien Teney, Damith Ranasinghe, and Ehsan Abbasnejad. 2025. Mysteries of the Deep: Role of Intermediate Representations in Out of Distribution Detection. Advances in Neural Information Processing Systems, Vol. 38 (2025), 110728-110762.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_55_1","volume-title":"Delving into out-of-distribution detection with vision-language representations. Advances in neural information processing systems","author":"Ming Yifei","year":"2022","unstructured":"Yifei Ming, Ziyang Cai, Jiuxiang Gu, Yiyou Sun, Wei Li, and Yixuan Li. 2022a. Delving into out-of-distribution detection with vision-language representations. Advances in neural information processing systems, Vol. 35 (2022), 35087-35102."},{"key":"e_1_3_2_1_56_1","volume-title":"How to exploit hyperspherical embeddings for out-of-distribution detection? arXiv preprint arXiv:2203.04450","author":"Ming Yifei","year":"2022","unstructured":"Yifei Ming, Yiyou Sun, Ousmane Dia, and Yixuan Li. 2022b. How to exploit hyperspherical embeddings for out-of-distribution detection? arXiv preprint arXiv:2203.04450 (2022)."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.52202\/075280-3335"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-025-02356-z"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5966"},{"key":"e_1_3_2_1_60_1","unstructured":"Yuval Netzer Tao Wang Adam Coates Alessandro Bissacco Bo Wu and Andrew Y Ng. 2011. Reading digits in natural images with unsupervised feature learning. (2011)."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00241"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.52202\/085713-0071"},{"key":"e_1_3_2_1_64_1","first-page":"150541","article-title":"OOD-Barrier: Build a Middle-Barrier for Open-Set Single-Image Test Time Adaptation via Vision Language Models","volume":"38","author":"Peng Boyang","year":"2025","unstructured":"Boyang Peng, Sanqing Qu, Tianpei Zou, Fan Lu, Ya Wu, Kai Chen, Siheng Chen, Yong Wu, and Guang Chen. 2025b. OOD-Barrier: Build a Middle-Barrier for Open-Set Single-Image Test Time Adaptation via Vision Language Models. Advances in Neural Information Processing Systems, Vol. 38 (2025), 150541-150560.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_65_1","volume-title":"PnPOOD: Out-of-distribution detection for text classification via plug andplay data augmentation. arXiv preprint arXiv:2111.00506","author":"Rawat Mrinal","year":"2021","unstructured":"Mrinal Rawat, Ramya Hebbalaguppe, and Lovekesh Vig. 2021. PnPOOD: Out-of-distribution detection for text classification via plug andplay data augmentation. arXiv preprint arXiv:2111.00506 (2021)."},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00020"},{"key":"e_1_3_2_1_67_1","volume-title":"International conference on machine learning. PMLR, 8491-8501","author":"Sastry Chandramouli Shama","year":"2020","unstructured":"Chandramouli Shama Sastry and Sageev Oore. 2020. Detecting out-of-distribution examples with gram matrices. In International conference on machine learning. PMLR, 8491-8501."},{"key":"e_1_3_2_1_68_1","volume-title":"Archana Sapkota, and Terrance E Boult.","author":"Scheirer Walter J","year":"2012","unstructured":"Walter J Scheirer, Anderson de Rezende Rocha, Archana Sapkota, and Terrance E Boult. 2012. Toward open set recognition. IEEE transactions on pattern analysis and machine intelligence, Vol. 35, 7 (2012), 1757-1772."},{"key":"e_1_3_2_1_69_1","volume-title":"React: Out-of-distribution detection with rectified activations. Advances in neural information processing systems","author":"Sun Yiyou","year":"2021","unstructured":"Yiyou Sun, Chuan Guo, and Yixuan Li. 2021. React: Out-of-distribution detection with rectified activations. Advances in neural information processing systems, Vol. 34 (2021), 144-157."},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20053-3_40"},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00914"},{"key":"e_1_3_2_1_72_1","volume-title":"Open-set recognition: A good closed-set classifier is all you need? arXiv preprint arXiv:2110.06207","author":"Vaze Sagar","year":"2021","unstructured":"Sagar Vaze, Kai Han, Andrea Vedaldi, and Andrew Zisserman. 2021a. Open-set recognition: A good closed-set classifier is all you need? arXiv preprint arXiv:2110.06207 (2021)."},{"key":"e_1_3_2_1_73_1","volume-title":"International conference on learning representations.","author":"Vaze Sagar","year":"2021","unstructured":"Sagar Vaze, Kai Han, Andrea Vedaldi, and Andrew Zisserman. 2021b. Open-set recognition: A good closed-set classifier is all you need. In International conference on learning representations."},{"key":"e_1_3_2_1_74_1","first-page":"162740","article-title":"DIsoN: Decentralized Isolation Networks for Out-of-Distribution Detection in Medical Imaging","volume":"38","author":"Wagner Felix","year":"2025","unstructured":"Felix Wagner, Pramit Saha, Harry Anthony, Alison Noble, and Konstantinos Kamnitsas. 2025. DIsoN: Decentralized Isolation Networks for Out-of-Distribution Detection in Medical Imaging. Advances in Neural Information Processing Systems, Vol. 38 (2025), 162740-162769.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00173"},{"key":"e_1_3_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00487"},{"key":"e_1_3_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00706"},{"key":"e_1_3_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.52202\/085713-2955"},{"key":"e_1_3_2_1_79_1","volume-title":"International conference on machine learning. PMLR, 23631-23644","author":"Wei Hongxin","year":"2022","unstructured":"Hongxin Wei, Renchunzi Xie, Hao Cheng, Lei Feng, Bo An, and Yixuan Li. 2022. Mitigating neural network overconfidence with logit normalization. In International conference on machine learning. PMLR, 23631-23644."},{"key":"e_1_3_2_1_80_1","first-page":"141958","article-title":"X-Mahalanobis: Transformer Feature Mixing for Reliable OOD Detection","volume":"38","author":"Wei Tong","year":"2025","unstructured":"Tong Wei, Bolin Wang, Jiang-Xin Shi, Yu-Feng Li, and Min-Ling Zhang. 2025. X-Mahalanobis: Transformer Feature Mixing for Reliable OOD Detection. Advances in Neural Information Processing Systems, Vol. 38 (2025), 141958-141986.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_81_1","volume-title":"Lmp-gan: Out-of-distribution detection for non-control data malware attacks","author":"Wood David","year":"2025","unstructured":"David Wood, David Kapp, Temesguen Messay-Kebede, and Keigo Hirakawa. 2025. Lmp-gan: Out-of-distribution detection for non-control data malware attacks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2025)."},{"key":"e_1_3_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2836950"},{"key":"e_1_3_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00819"},{"key":"e_1_3_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2362"},{"key":"e_1_3_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-024-02117-4"},{"key":"e_1_3_2_1_86_1","volume-title":"Forty-second International Conference on Machine Learning.","author":"Yang Yang","year":"2025","unstructured":"Yang Yang and Haonan Xu. 2025. Strengthen out-of-distribution detection capability with progressive self-knowledge distillation. In Forty-second International Conference on Machine Learning."},{"key":"e_1_3_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00961"},{"key":"e_1_3_2_1_88_1","doi-asserted-by":"publisher","DOI":"10.52202\/075280-2549"},{"key":"e_1_3_2_1_89_1","first-page":"163483","article-title":"Human texts are outliers: Detecting llm-generated texts via out-of-distribution detection","volume":"38","author":"Zeng Cong","year":"2025","unstructured":"Cong Zeng, Shengkun Tang, Yuanzhou Chen, Zhiqiang Shen, Wenchao Yu, Xujiang Zhao, Haifeng Chen, Wei Cheng, and Zhiqiang Xu. 2025. Human texts are outliers: Detecting llm-generated texts via out-of-distribution detection. Advances in Neural Information Processing Systems, Vol. 38 (2025), 163483-163513.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_90_1","first-page":"118006","article-title":"Strap: Spatio-temporal pattern retrieval for out-of-distribution generalization","volume":"38","author":"Zhang Haoyu","year":"2025","unstructured":"Haoyu Zhang, Hao Miao, Xinke Jiang, Yuchen Fang, and Yifan Zhang. 2025. Strap: Spatio-temporal pattern retrieval for out-of-distribution generalization. Advances in Neural Information Processing Systems, Vol. 38 (2025), 118006-118041.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_91_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Zhang Jinsong","year":"2022","unstructured":"Jinsong Zhang, Qiang Fu, Xu Chen, Lun Du, Zelin Li, Gang Wang, Shi Han, Dongmei Zhang, et al., 2022. Out-of-distribution detection based on in-distribution data patterns memorization with modern hopfield energy. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00549"},{"key":"e_1_3_2_1_93_1","unstructured":"Jingyang Zhang Jingkang Yang Pengyun Wang Haoqi Wang Yueqian Lin Haoran Zhang Yiyou Sun Xuefeng Du Yixuan Li Ziwei Liu et al. 2023b. Openood v1. 5: Enhanced benchmark for out-of-distribution detection. arXiv preprint arXiv:2306.09301 (2023)."},{"key":"e_1_3_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2020.2983593"},{"key":"e_1_3_2_1_95_1","volume-title":"Places: A 10 million image database for scene recognition","author":"Zhou Bolei","year":"2017","unstructured":"Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba. 2017. Places: A 10 million image database for scene recognition. IEEE transactions on pattern analysis and machine intelligence, Vol. 40, 6 (2017), 1452-1464."},{"key":"e_1_3_2_1_96_1","first-page":"80353","article-title":"(\u0394)Energy: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization","volume":"38","author":"Zhu Lin","year":"2025","unstructured":"Lin Zhu, Yifeng Yang, Xinbing Wang, Qinying Gu, and Nanyang Ye. 2025. (\u0394)Energy: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization. Advances in Neural Information Processing Systems, Vol. 38 (2025), 80353-80386.","journal-title":"Advances in Neural Information Processing Systems"}],"event":{"name":"KDD '26: The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Jeju Island Republic of Korea","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"deposited":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T01:33:44Z","timestamp":1786066424000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3770855.3816455"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,8]]},"references-count":96,"alternative-id":["10.1145\/3770855.3816455","10.1145\/3770855"],"URL":"https:\/\/doi.org\/10.1145\/3770855.3816455","relation":{},"subject":[],"published":{"date-parts":[[2026,8,8]]},"assertion":[{"value":"2026-08-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}