{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:24:40Z","timestamp":1750220680750,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":30,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,3,7]],"date-time":"2021-03-07T00:00:00Z","timestamp":1615075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62002325"],"award-info":[{"award-number":["62002325"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LQ18F030013, LQ18F030014"],"award-info":[{"award-number":["LQ18F030013, LQ18F030014"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,3,7]]},"DOI":"10.1145\/3444685.3446311","type":"proceedings-article","created":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T04:48:41Z","timestamp":1620103721000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Improving auto-encoder novelty detection using channel attention and entropy minimization"],"prefix":"10.1145","author":[{"given":"Miao","family":"Tian","sequence":"first","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China"}]},{"given":"Dongyan","family":"Guo","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China"}]},{"given":"Ying","family":"Cui","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China"}]},{"given":"Xiang","family":"Pan","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China"}]},{"given":"Shengyong","family":"Chen","sequence":"additional","affiliation":[{"name":"Tianjin University of Technology, Tianjin, China"}]}],"member":"320","published-online":{"date-parts":[[2021,5,3]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"And: Autoregressive novelty detectors. arXiv preprint arXiv:1807.01653","author":"Abati Davide","year":"2018","unstructured":"Davide Abati , Angelo Porrello , Simone Calderara , and Rita Cucchiara . 2018 . And: Autoregressive novelty detectors. arXiv preprint arXiv:1807.01653 (2018). Davide Abati, Angelo Porrello, Simone Calderara, and Rita Cucchiara. 2018. And: Autoregressive novelty detectors. arXiv preprint arXiv:1807.01653 (2018)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00057"},{"key":"e_1_3_2_1_3_1","volume-title":"Asian conference on computer vision. Springer, 622--637","author":"Akcay Samet","year":"2018","unstructured":"Samet Akcay , Amir Atapour-Abarghouei , and Toby P Breckon . 2018 . Ganomaly: Semi-supervised anomaly detection via adversarial training . In Asian conference on computer vision. Springer, 622--637 . Samet Akcay, Amir Atapour-Abarghouei, and Toby P Breckon. 2018. Ganomaly: Semi-supervised anomaly detection via adversarial training. In Asian conference on computer vision. Springer, 622--637."},{"key":"e_1_3_2_1_4_1","volume-title":"Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly detection. arXiv preprint arXiv:1901.08954","author":"Ak\u00e7ay Samet","year":"2019","unstructured":"Samet Ak\u00e7ay , Amir Atapour-Abarghouei , and Toby P Breckon . 2019 . Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly detection. arXiv preprint arXiv:1901.08954 (2019). Samet Ak\u00e7ay, Amir Atapour-Abarghouei, and Toby P Breckon. 2019. Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly detection. arXiv preprint arXiv:1901.08954 (2019)."},{"key":"e_1_3_2_1_5_1","volume-title":"Variational autoencoder based anomaly detection using reconstruction probability. Special Lecture on IE 2, 1","author":"An Jinwon","year":"2015","unstructured":"Jinwon An and Sungzoon Cho . 2015. Variational autoencoder based anomaly detection using reconstruction probability. Special Lecture on IE 2, 1 ( 2015 ). Jinwon An and Sungzoon Cho. 2015. Variational autoencoder based anomaly detection using reconstruction probability. Special Lecture on IE 2, 1 (2015)."},{"key":"e_1_3_2_1_6_1","unstructured":"Christopher M Bishop. 2006. Pattern recognition and machine learning. springer.  Christopher M Bishop. 2006. Pattern recognition and machine learning. springer."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335388"},{"key":"e_1_3_2_1_8_1","volume-title":"Xiang Sean Zhou, and Thomas S Huang","author":"Chen Yunqiang","year":"2001","unstructured":"Yunqiang Chen , Xiang Sean Zhou, and Thomas S Huang . 2001 . One-class SVM for learning in image retrieval.. In ICIP (1). Citeseer , 34--37. Yunqiang Chen, Xiang Sean Zhou, and Thomas S Huang. 2001. One-class SVM for learning in image retrieval.. In ICIP (1). Citeseer, 34--37."},{"key":"e_1_3_2_1_9_1","unstructured":"Izhak Golan and Ran El-Yaniv. 2018. Deep Anomaly Detection Using Geometric Transformations. (2018).  Izhak Golan and Ran El-Yaniv. 2018. Deep Anomaly Detection Using Geometric Transformations. (2018)."},{"key":"e_1_3_2_1_10_1","first-page":"2672","article-title":"Generative Adversarial Networks","volume":"3","author":"Goodfellow Ian J","year":"2014","unstructured":"Ian J Goodfellow , Jean Pouget-Abadie , Mehdi Mirza , Bing Xu , David Warde-Farley , Sherjil Ozair , Aaron Courville , and Yoshua Bengio . 2014 . Generative Adversarial Networks . Advances in Neural Information Processing Systems 3 (2014), 2672 -- 2680 . Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative Adversarial Networks. Advances in Neural Information Processing Systems 3 (2014), 2672--2680.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_11_1","volume-title":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)","volume":"2","author":"Hadsell Raia","year":"2006","unstructured":"Raia Hadsell , Sumit Chopra , and Yann LeCun . 2006 . Dimensionality reduction by learning an invariant mapping . In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) , Vol. 2 . IEEE, 1735--1742. Raia Hadsell, Sumit Chopra, and Yann LeCun. 2006. Dimensionality reduction by learning an invariant mapping. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), Vol. 2. IEEE, 1735--1742."},{"key":"e_1_3_2_1_12_1","unstructured":"Dan Hendrycks Mantas Mazeika Saurav Kadavath and Dawn Song. 2019. Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty. (2019).  Dan Hendrycks Mantas Mazeika Saurav Kadavath and Dawn Song. 2019. Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty. (2019)."},{"key":"e_1_3_2_1_13_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling . 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 ( 2013 ). Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00684"},{"key":"e_1_3_2_1_15_1","volume-title":"Active authentication using an autoencoder regularized cnn-based one-class classifier. arXiv preprint arXiv:1903.01031","author":"Oza Poojan","year":"2019","unstructured":"Poojan Oza and Vishal M Patel . 2019. Active authentication using an autoencoder regularized cnn-based one-class classifier. arXiv preprint arXiv:1903.01031 ( 2019 ). Poojan Oza and Vishal M Patel. 2019. Active authentication using an autoencoder regularized cnn-based one-class classifier. arXiv preprint arXiv:1903.01031 (2019)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00301"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/BTAS.2018.8698603"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2917862"},{"key":"e_1_3_2_1_19_1","volume-title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Computer Science","author":"Radford Alec","year":"2015","unstructured":"Alec Radford , Luke Metz , and Soumith Chintala . 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Computer Science ( 2015 ). Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Computer Science (2015)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1049\/ip-vis:19990428"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Olaf Ronneberger Philipp Fischer and Thomas Brox. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. (2015).  Olaf Ronneberger Philipp Fischer and Thomas Brox. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. (2015).","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00356"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2689746.2689747"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"e_1_3_2_1_25_1","unstructured":"M Tribus. 1961. Thermostatics and Thermodynamics: An Introduction to Energy. In Information and Sates of Matter with Engineering Applications. D. van Nostrand Princeton.  M Tribus. 1961. Thermostatics and Thermodynamics: An Introduction to Energy. In Information and Sates of Matter with Engineering Applications. D. van Nostrand Princeton."},{"key":"e_1_3_2_1_26_1","unstructured":"Aaron Van den Oord Nal Kalchbrenner Lasse Espeholt Oriol Vinyals Alex Graves etal 2016. Conditional image generation with pixelcnn decoders. In Advances in neural information processing systems. 4790--4798.  Aaron Van den Oord Nal Kalchbrenner Lasse Espeholt Oriol Vinyals Alex Graves et al. 2016. Conditional image generation with pixelcnn decoders. In Advances in neural information processing systems. 4790--4798."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.683"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"e_1_3_2_1_29_1","volume-title":"Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar.","author":"Zenati Houssam","year":"2018","unstructured":"Houssam Zenati , Chuan Sheng Foo , Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar. 2018 . Efficient gan-based anomaly detection. arXiv preprint arXiv:1802.06222 (2018). Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar. 2018. Efficient gan-based anomaly detection. arXiv preprint arXiv:1802.06222 (2018)."},{"key":"e_1_3_2_1_30_1","volume-title":"Self-attention generative adversarial networks. arXiv preprint arXiv:1805.08318","author":"Zhang Han","year":"2018","unstructured":"Han Zhang , Ian Goodfellow , Dimitris Metaxas , and Augustus Odena . 2018. Self-attention generative adversarial networks. arXiv preprint arXiv:1805.08318 ( 2018 ). Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena. 2018. Self-attention generative adversarial networks. arXiv preprint arXiv:1805.08318 (2018)."}],"event":{"name":"MMAsia '20: ACM Multimedia Asia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Virtual Event Singapore","acronym":"MMAsia '20"},"container-title":["Proceedings of the 2nd ACM International Conference on Multimedia in Asia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3444685.3446311","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3444685.3446311","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:03:20Z","timestamp":1750197800000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3444685.3446311"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,7]]},"references-count":30,"alternative-id":["10.1145\/3444685.3446311","10.1145\/3444685"],"URL":"https:\/\/doi.org\/10.1145\/3444685.3446311","relation":{},"subject":[],"published":{"date-parts":[[2021,3,7]]},"assertion":[{"value":"2021-05-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}