{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T11:25:42Z","timestamp":1764588342941,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":55,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Major Basic Research Project of Natural Science Foundation of Shandong Province","award":["ZR2021ZD15"],"award-info":[{"award-number":["ZR2021ZD15"]}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR202102240155, ZR2021QF001"],"award-info":[{"award-number":["ZR202102240155, ZR2021QF001"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Young Elite Scientists Sponsorship Program by CAST","award":["2021QNRC001"],"award-info":[{"award-number":["2021QNRC001"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176139, 61876098, 62106128"],"award-info":[{"award-number":["62176139, 61876098, 62106128"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,10]]},"DOI":"10.1145\/3503161.3547815","type":"proceedings-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T15:42:35Z","timestamp":1665416555000},"page":"4242-4251","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["RONF: Reliable Outlier Synthesis under Noisy Feature Space for Out-of-Distribution Detection"],"prefix":"10.1145","author":[{"given":"Rundong","family":"He","sequence":"first","affiliation":[{"name":"Shandong University, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongyi","family":"Han","sequence":"additional","affiliation":[{"name":"Shandong University, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiankai","family":"Lu","sequence":"additional","affiliation":[{"name":"Shandong University, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yilong","family":"Yin","sequence":"additional","affiliation":[{"name":"Shandong University, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Learning and generalization in overparameterized neural networks, going beyond two layers. Advances in neural information processing systems 32","author":"Allen-Zhu Zeyuan","year":"2019","unstructured":"Zeyuan Allen-Zhu , Yuanzhi Li , and Yingyu Liang . 2019. Learning and generalization in overparameterized neural networks, going beyond two layers. Advances in neural information processing systems 32 ( 2019 ). Zeyuan Allen-Zhu, Yuanzhi Li, and Yingyu Liang. 2019. Learning and generalization in overparameterized neural networks, going beyond two layers. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_2_2_1","volume-title":"Informative outlier matters: Robustifying out-of-distribution detection using outlier mining. arXiv preprint arXiv:2006.15207","author":"Chen Jiefeng","year":"2020","unstructured":"Jiefeng Chen , Yixuan Li , XiWu, Yingyu Liang , and Somesh Jha . 2020. Informative outlier matters: Robustifying out-of-distribution detection using outlier mining. arXiv preprint arXiv:2006.15207 ( 2020 ). Jiefeng Chen, Yixuan Li, XiWu, Yingyu Liang, and Somesh Jha. 2020. Informative outlier matters: Robustifying out-of-distribution detection using outlier mining. arXiv preprint arXiv:2006.15207 (2020)."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86523-8_26"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.461"},{"key":"e_1_3_2_2_5_1","volume-title":"Reducing network agnostophobia. Advances in Neural Information Processing Systems 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 31 ( 2018 ). Akshay Raj Dhamija, Manuel G\u00fcnther, and Terrance Boult. 2018. Reducing network agnostophobia. Advances in Neural Information Processing Systems 31 (2018)."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v38i3.2756"},{"key":"e_1_3_2_2_7_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). 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_2_8_1","volume-title":"Exploring the limits of out-of-distribution detection. Advances in Neural Information Processing Systems 34","author":"Fort Stanislav","year":"2021","unstructured":"Stanislav Fort , Jie Ren , and Balaji Lakshminarayanan . 2021. Exploring the limits of out-of-distribution detection. Advances in Neural Information Processing Systems 34 ( 2021 ). Stanislav Fort, Jie Ren, and Balaji Lakshminarayanan. 2021. Exploring the limits of out-of-distribution detection. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_3_2_2_9_1","volume-title":"ImageNet-trained CNNs are biased towards texture","author":"Geirhos Robert","year":"1811","unstructured":"Robert Geirhos , Patricia Rubisch , Claudio Michaelis , Matthias Bethge , Felix A Wichmann , and Wieland Brendel . 2018. ImageNet-trained CNNs are biased towards texture ; increasing shape bias improves accuracy and robustness. arXiv preprint arXiv: 1811 .12231 (2018). Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel. 2018. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. arXiv preprint arXiv:1811.12231 (2018)."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3416276"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01418"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20644"},{"key":"e_1_3_2_2_13_1","volume-title":"Towards safe and robust weakly-supervised anomaly detection under subpopulation shift. Knowledge- Based Systems","author":"He Rundong","year":"2022","unstructured":"Rundong He , Zhongyi Han , and Yilong Yin . 2022. Towards safe and robust weakly-supervised anomaly detection under subpopulation shift. Knowledge- Based Systems ( 2022 ), 109088. Rundong He, Zhongyi Han, and Yilong Yin. 2022. Towards safe and robust weakly-supervised anomaly detection under subpopulation shift. Knowledge- Based Systems (2022), 109088."},{"key":"e_1_3_2_2_14_1","volume-title":"Abaseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136","author":"Hendrycks Dan","year":"2016","unstructured":"Dan Hendrycks and Kevin Gimpel . 2016. Abaseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136 ( 2016 ). Dan Hendrycks and Kevin Gimpel. 2016. Abaseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136 (2016)."},{"key":"e_1_3_2_2_15_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 ). 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_2_16_1","volume-title":"On the Importance of Gradients for Detecting Distributional Shifts in the Wild. arXiv preprint arXiv:2110.00218","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. arXiv preprint arXiv:2110.00218 ( 2021 ). Rui Huang, Andrew Geng, and Yixuan Li. 2021. On the Importance of Gradients for Detecting Distributional Shifts in the Wild. arXiv preprint arXiv:2110.00218 (2021)."},{"key":"e_1_3_2_2_17_1","volume-title":"Adversarial examples are not bugs, they are features. Advances in neural information processing systems 32","author":"Ilyas Andrew","year":"2019","unstructured":"Andrew Ilyas , Shibani Santurkar , Dimitris Tsipras , Logan Engstrom , Brandon Tran , and Aleksander Madry . 2019. Adversarial examples are not bugs, they are features. Advances in neural information processing systems 32 ( 2019 ). Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry. 2019. Adversarial examples are not bugs, they are features. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_2_18_1","unstructured":"Alex Krizhevsky Geoffrey Hinton etal 2009. Learning multiple layers of features from tiny images. Citeseer (2009).  Alex Krizhevsky Geoffrey Hinton et al. 2009. Learning multiple layers of features from tiny images. Citeseer (2009)."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126543"},{"key":"e_1_3_2_2_20_1","volume-title":"Training confidencecalibrated classifiers for detecting out-of-distribution samples. arXiv preprint arXiv:1711.09325","author":"Lee Kimin","year":"2017","unstructured":"Kimin Lee , Honglak Lee , Kibok Lee , and Jinwoo Shin . 2017. Training confidencecalibrated classifiers for detecting out-of-distribution samples. arXiv preprint arXiv:1711.09325 ( 2017 ). Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin. 2017. Training confidencecalibrated classifiers for detecting out-of-distribution samples. arXiv preprint arXiv:1711.09325 (2017)."},{"key":"e_1_3_2_2_21_1","volume-title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks. Advances in neural information processing systems 31","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 31 ( 2018 ). 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 31 (2018)."},{"key":"e_1_3_2_2_22_1","volume-title":"Removing undesirable feature contributions using out-ofdistribution data. arXiv preprint arXiv:2101.06639","author":"Lee Saehyung","year":"2021","unstructured":"Saehyung Lee , Changhwa Park , Hyungyu Lee , Jihun Yi , Jonghyun Lee , and Sungroh Yoon . 2021. Removing undesirable feature contributions using out-ofdistribution data. arXiv preprint arXiv:2101.06639 ( 2021 ). Saehyung Lee, Changhwa Park, Hyungyu Lee, Jihun Yi, Jonghyun Lee, and Sungroh Yoon. 2021. Removing undesirable feature contributions using out-ofdistribution data. arXiv preprint arXiv:2101.06639 (2021)."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413641"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01323"},{"key":"e_1_3_2_2_25_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 ). 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_2_26_1","volume-title":"Energy-based out-of-distribution detection. arXiv preprint arXiv:2010.03759","author":"Liu Weitang","year":"2020","unstructured":"Weitang Liu , Xiaoyun Wang , John D Owens , and Yixuan Li. 2020. Energy-based out-of-distribution detection. arXiv preprint arXiv:2010.03759 ( 2020 ). Weitang Liu, Xiaoyun Wang, John D Owens, and Yixuan Li. 2020. Energy-based out-of-distribution detection. arXiv preprint arXiv:2010.03759 (2020)."},{"key":"e_1_3_2_2_27_1","volume-title":"On the impact of spurious correlation for out-of-distribution detection. arXiv preprint arXiv:2109.05642","author":"Ming Yifei","year":"2021","unstructured":"Yifei Ming , Hang Yin , and Yixuan Li. 2021. On the impact of spurious correlation for out-of-distribution detection. arXiv preprint arXiv:2109.05642 ( 2021 ). Yifei Ming, Hang Yin, and Yixuan Li. 2021. On the impact of spurious correlation for out-of-distribution detection. arXiv preprint arXiv:2109.05642 (2021)."},{"key":"e_1_3_2_2_28_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"34","author":"Mohseni Sina","year":"2020","unstructured":"Sina Mohseni , Mandar Pitale , JBS Yadawa , and Zhangyang Wang . 2020 . Selfsupervised learning for generalizable out-of-distribution detection . In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 34 . 5216--5223. Sina Mohseni, Mandar Pitale, JBS Yadawa, and Zhangyang Wang. 2020. Selfsupervised learning for generalizable out-of-distribution detection. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 5216--5223."},{"key":"e_1_3_2_2_29_1","volume-title":"Provable Guarantees for Understanding Out-of-distribution Detection. arXiv preprint arXiv:2112.00787","author":"Morteza Peyman","year":"2021","unstructured":"Peyman Morteza and Yixuan Li. 2021. Provable Guarantees for Understanding Out-of-distribution Detection. arXiv preprint arXiv:2112.00787 ( 2021 ). Peyman Morteza and Yixuan Li. 2021. Provable Guarantees for Understanding Out-of-distribution Detection. arXiv preprint arXiv:2112.00787 (2021)."},{"key":"e_1_3_2_2_30_1","volume-title":"Understanding the failure modes of out-of-distribution generalization. arXiv preprint arXiv:2010.15775","author":"Nagarajan Vaishnavh","year":"2020","unstructured":"Vaishnavh Nagarajan , Anders Andreassen , and Behnam Neyshabur . 2020. Understanding the failure modes of out-of-distribution generalization. arXiv preprint arXiv:2010.15775 ( 2020 ). Vaishnavh Nagarajan, Anders Andreassen, and Behnam Neyshabur. 2020. Understanding the failure modes of out-of-distribution generalization. arXiv preprint arXiv:2010.15775 (2020)."},{"key":"e_1_3_2_2_31_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).  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_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"e_1_3_2_2_33_1","volume-title":"Task- Agnostic Undesirable Feature Deactivation Using Out-of-Distribution Data. Advances in Neural Information Processing Systems 34","author":"Park Dongmin","year":"2021","unstructured":"Dongmin Park , Hwanjun Song , MinSeok Kim , and Jae-Gil Lee . 2021. Task- Agnostic Undesirable Feature Deactivation Using Out-of-Distribution Data. Advances in Neural Information Processing Systems 34 ( 2021 ). Dongmin Park, Hwanjun Song, MinSeok Kim, and Jae-Gil Lee. 2021. Task- Agnostic Undesirable Feature Deactivation Using Out-of-Distribution Data. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_3_2_2_34_1","volume-title":"Likelihood ratios for out-of-distribution detection. arXiv preprint arXiv:1906.02845","author":"Ren Jie","year":"2019","unstructured":"Jie Ren , Peter J Liu , Emily Fertig , Jasper Snoek , Ryan Poplin , Mark A DePristo , Joshua V Dillon , and Balaji Lakshminarayanan . 2019. Likelihood ratios for out-of-distribution detection. arXiv preprint arXiv:1906.02845 ( 2019 ). Jie Ren, Peter J Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A DePristo, Joshua V Dillon, and Balaji Lakshminarayanan. 2019. Likelihood ratios for out-of-distribution detection. arXiv preprint arXiv:1906.02845 (2019)."},{"key":"e_1_3_2_2_35_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 . 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_2_36_1","volume-title":"A less biased evaluation of out-of-distribution sample detectors. arXiv preprint arXiv:1809.04729","author":"Shafaei Alireza","year":"2018","unstructured":"Alireza Shafaei , Mark Schmidt , and James J Little . 2018. A less biased evaluation of out-of-distribution sample detectors. arXiv preprint arXiv:1809.04729 ( 2018 ). Alireza Shafaei, Mark Schmidt, and James J Little. 2018. A less biased evaluation of out-of-distribution sample detectors. arXiv preprint arXiv:1809.04729 (2018)."},{"key":"e_1_3_2_2_37_1","volume-title":"Amir Roshan Zamir, and Mubarak Shah","author":"Soomro Khurram","year":"2012","unstructured":"Khurram Soomro , Amir Roshan Zamir, and Mubarak Shah . 2012 . A dataset of 101 human action classes from videos in the wild. Center for Research in Computer Vision 2, 11 (2012). Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. 2012. A dataset of 101 human action classes from videos in the wild. Center for Research in Computer Vision 2, 11 (2012)."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475472"},{"key":"e_1_3_2_2_39_1","volume-title":"React: Out-of-distribution detection with rectified activations. Advances in Neural Information Processing Systems 34","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 34 (2021). Yiyou Sun, Chuan Guo, and Yixuan Li. 2021. React: Out-of-distribution detection with rectified activations. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_3_2_2_40_1","volume-title":"Csi: Novelty detection via contrastive learning on distributionally shifted instances. Advances in neural information processing systems 33","author":"Tack Jihoon","year":"2020","unstructured":"Jihoon Tack , Sangwoo Mo , Jongheon Jeong , and Jinwoo Shin . 2020 . Csi: Novelty detection via contrastive learning on distributionally shifted instances. Advances in neural information processing systems 33 (2020), 11839--11852. Jihoon Tack, Sangwoo Mo, Jongheon Jeong, and Jinwoo Shin. 2020. Csi: Novelty detection via contrastive learning on distributionally shifted instances. Advances in neural information processing systems 33 (2020), 11839--11852."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00119"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.510"},{"key":"e_1_3_2_2_43_1","volume-title":"Out-of-distribution detection in classifiers via generation. arXiv preprint arXiv:1910.04241","author":"Vernekar Sachin","year":"2019","unstructured":"Sachin Vernekar , Ashish Gaurav , Vahdat Abdelzad , Taylor Denouden , Rick Salay , and Krzysztof Czarnecki . 2019. Out-of-distribution detection in classifiers via generation. arXiv preprint arXiv:1910.04241 ( 2019 ). Sachin Vernekar, Ashish Gaurav, Vahdat Abdelzad, Taylor Denouden, Rick Salay, and Krzysztof Czarnecki. 2019. Out-of-distribution detection in classifiers via generation. arXiv preprint arXiv:1910.04241 (2019)."},{"key":"e_1_3_2_2_44_1","volume-title":"International Conference on Learning Representations.","author":"Xia Xiaobo","year":"2020","unstructured":"Xiaobo Xia , Tongliang Liu , Bo Han , Chen Gong , Nannan Wang , Zongyuan Ge , and Yi Chang . 2020 . Robust early-learning: Hindering the memorization of noisy labels . In International Conference on Learning Representations. Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, and Yi Chang. 2020. Robust early-learning: Hindering the memorization of noisy labels. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_45_1","volume-title":"Likelihood regret: An out-of-distribution detection score for variational auto-encoder. arXiv preprint arXiv:2003.02977","author":"Xiao Zhisheng","year":"2020","unstructured":"Zhisheng Xiao , Qing Yan , and Yali Amit . 2020. Likelihood regret: An out-of-distribution detection score for variational auto-encoder. arXiv preprint arXiv:2003.02977 ( 2020 ). Zhisheng Xiao, Qing Yan, and Yali Amit. 2020. Likelihood regret: An out-of-distribution detection score for variational auto-encoder. arXiv preprint arXiv:2003.02977 (2020)."},{"key":"e_1_3_2_2_46_1","volume-title":"Turkergaze: Crowdsourcing saliency with webcam based eye tracking. arXiv preprint arXiv:1504.06755","author":"Xu Pingmei","year":"2015","unstructured":"Pingmei Xu , Krista A Ehinger , Yinda Zhang , Adam Finkelstein , Sanjeev R Kulkarni , and Jianxiong Xiao . 2015 . Turkergaze: Crowdsourcing saliency with webcam based eye tracking. arXiv preprint arXiv:1504.06755 (2015). Pingmei Xu, Krista A Ehinger, Yinda Zhang, Adam Finkelstein, Sanjeev R Kulkarni, and Jianxiong Xiao. 2015. Turkergaze: Crowdsourcing saliency with webcam based eye tracking. arXiv preprint arXiv:1504.06755 (2015)."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00819"},{"key":"e_1_3_2_2_48_1","volume-title":"Generalized Out-of-Distribution Detection: A Survey. arXiv preprint arXiv:2110.11334","author":"Yang Jingkang","year":"2021","unstructured":"Jingkang Yang , Kaiyang Zhou , Yixuan Li , and Ziwei Liu . 2021. Generalized Out-of-Distribution Detection: A Survey. arXiv preprint arXiv:2110.11334 ( 2021 ). Jingkang Yang, Kaiyang Zhou, Yixuan Li, and Ziwei Liu. 2021. Generalized Out-of-Distribution Detection: A Survey. arXiv preprint arXiv:2110.11334 (2021)."},{"key":"e_1_3_2_2_49_1","volume-title":"Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning. arXiv preprint arXiv:2202.11915","author":"Yang Xihong","year":"2022","unstructured":"Xihong Yang , Xiaochang Hu , Sihang Zhou , Xinwang Liu , and En Zhu . 2022. Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning. arXiv preprint arXiv:2202.11915 ( 2022 ). Xihong Yang, Xiaochang Hu, Sihang Zhou, Xinwang Liu, and En Zhu. 2022. Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning. arXiv preprint arXiv:2202.11915 (2022)."},{"key":"e_1_3_2_2_50_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 , Ari Seff , Yinda Zhang , Shuran Song , Thomas Funkhouser , 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). Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, 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_2_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00961"},{"key":"e_1_3_2_2_52_1","volume-title":"Can Subnetwork Structure be the Key to Out-of-Distribution Generalization? arXiv preprint arXiv:2106.02890","author":"Zhang Dinghuai","year":"2021","unstructured":"Dinghuai Zhang , Kartik Ahuja , Yilun Xu , YisenWang, and Aaron Courville . 2021. Can Subnetwork Structure be the Key to Out-of-Distribution Generalization? arXiv preprint arXiv:2106.02890 ( 2021 ). Dinghuai Zhang, Kartik Ahuja, Yilun Xu, YisenWang, and Aaron Courville. 2021. Can Subnetwork Structure be the Key to Out-of-Distribution Generalization? arXiv preprint arXiv:2106.02890 (2021)."},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2020.3039798"},{"key":"e_1_3_2_2_54_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 40, 6 (2017), 1452--1464. 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 40, 6 (2017), 1452--1464."},{"key":"e_1_3_2_2_55_1","volume-title":"STEP: Out-of-Distribution Detection in the Presence of Limited In-distribution Labeled Data. Advances in Neural Information Processing Systems 34","author":"Zhou Zhi","year":"2021","unstructured":"Zhi Zhou , Lan-Zhe Guo , Zhanzhan Cheng , Yu-Feng Li , and Shiliang Pu . 2021 . STEP: Out-of-Distribution Detection in the Presence of Limited In-distribution Labeled Data. Advances in Neural Information Processing Systems 34 (2021). Zhi Zhou, Lan-Zhe Guo, Zhanzhan Cheng, Yu-Feng Li, and Shiliang Pu. 2021. STEP: Out-of-Distribution Detection in the Presence of Limited In-distribution Labeled Data. Advances in Neural Information Processing Systems 34 (2021)."}],"event":{"name":"MM '22: The 30th ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Lisboa Portugal","acronym":"MM '22"},"container-title":["Proceedings of the 30th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3547815","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3503161.3547815","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:34Z","timestamp":1750186954000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3547815"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":55,"alternative-id":["10.1145\/3503161.3547815","10.1145\/3503161"],"URL":"https:\/\/doi.org\/10.1145\/3503161.3547815","relation":{},"subject":[],"published":{"date-parts":[[2022,10,10]]},"assertion":[{"value":"2022-10-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}