{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:04:40Z","timestamp":1750309480020,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":34,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T00:00:00Z","timestamp":1695340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,9,22]]},"DOI":"10.1145\/3641584.3641615","type":"proceedings-article","created":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T22:44:43Z","timestamp":1718405083000},"page":"205-211","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Active Learning Image Classification Algorithm Based on Class-Wise Self-Knowledge Distillation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6120-9991","authenticated-orcid":false,"given":"Yuliang","family":"Pang","sequence":"first","affiliation":[{"name":"Center for Image and Information Processing, Xi'an University of Posts and Telecommunications, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9037-7818","authenticated-orcid":false,"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"Center for Image and Information Processing,International Joint-Research Center for Wireless Communication and Information Processing, Xi'an University of Posts and Telecommunications, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6820-5243","authenticated-orcid":false,"given":"Yu","family":"Hao","sequence":"additional","affiliation":[{"name":"Center for Image and Information Processing, Xi'an University of Posts and Telecommunications, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1620-1101","authenticated-orcid":false,"given":"Yanchao","family":"Gong","sequence":"additional","affiliation":[{"name":"Center for Image and Information Processing, Xi'an University of Posts and Telecommunications, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5766-5973","authenticated-orcid":false,"given":"Daxiang","family":"Li","sequence":"additional","affiliation":[{"name":"Center for Image and Information Processing, Xi'an University of Posts and Telecommunications, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0524-5926","authenticated-orcid":false,"given":"Zhijie","family":"Xu","sequence":"additional","affiliation":[{"name":"International Joint-Research Center for Wireless Communication and Information Processing, University of Huddersfield, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,6,14]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"A survey of deep active learning[J]. ACM computing surveys (CSUR)","author":"REN P","year":"2021","unstructured":"REN P, XIAO Y, CHANG X, HUANG P-Y, LI Z, GUPTA B B, CHEN X, WANG X. A survey of deep active learning[J]. ACM computing surveys (CSUR), 2021, 54(9): 1-40."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"YOO D KWEON I S. Learning loss for active learning[C]\/\/Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2019: 93-102.","DOI":"10.1109\/CVPR.2019.00018"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"SINHA S EBRAHIMI S DARRELL T. Variational adversarial active learning[C]\/\/Proceedings of the IEEE\/CVF International Conference on Computer Vision. 2019: 5972-5981.","DOI":"10.1109\/ICCV.2019.00607"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"KIM K PARK D KIM K I CHUN S Y. Task-aware variational adversarial active learning[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2021: 8166-8175.","DOI":"10.1109\/CVPR46437.2021.00807"},{"key":"e_1_3_2_1_5_1","volume-title":"A deep active learning system for species identification and counting in camera trap images[J]. Methods in ecology and evolution","author":"NOROUZZADEH M S","year":"2021","unstructured":"NOROUZZADEH M S, MORRIS D, BEERY S, JOSHI N, JOJIC N, CLUNE J. A deep active learning system for species identification and counting in camera trap images[J]. Methods in ecology and evolution, 2021, 12(1): 150-161."},{"key":"e_1_3_2_1_6_1","volume-title":"GUAN C. Dsal: Deeply supervised active learning from strong and weak labelers for biomedical image segmentation[J]","author":"ZHAO Z","year":"2021","unstructured":"ZHAO Z, ZENG Z, XU K, CHEN C, GUAN C. Dsal: Deeply supervised active learning from strong and weak labelers for biomedical image segmentation[J]. IEEE journal of biomedical and health informatics, 2021, 25(10): 3744-3751."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"YUAN T WAN F FU M LIU J XU S JI X YE Q. Multiple instance active learning for object detection[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2021: 5330-5339.","DOI":"10.1109\/CVPR46437.2021.00529"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2016.2589879"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"HUANG S WANG T XIONG H HUAN J DOU D. Semi-supervised active learning with temporal output discrepancy[C]\/\/Proceedings of the IEEE\/CVF International Conference on Computer Vision. 2021: 3447-3456.","DOI":"10.1109\/ICCV48922.2021.00343"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02515-y"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2018.09.001"},{"key":"e_1_3_2_1_12_1","first-page":"171","article-title":"Deep evidential active learning for image classification[J]","volume":"3","author":"HEMMER P","year":"2022","unstructured":"[HEMMER P, K\u00fcHL N, SCH\u00f6FFER J. Deal: Deep evidential active learning for image classification[J]. Deep Learning Applications, Volume 3, 2022: 171-192.","journal-title":"Deep Learning Applications"},{"volume-title":"Combining active learning and data augmentation for image classification[C]\/\/Proceedings of the 2020 3rd International Conference on Big Data Technologies. 2020: 58-62","author":"MA Y","key":"e_1_3_2_1_13_1","unstructured":"MA Y, LU S, XU E, YU T, ZHOU L. Combining active learning and data augmentation for image classification[C]\/\/Proceedings of the 2020 3rd International Conference on Big Data Technologies. 2020: 58-62."},{"key":"e_1_3_2_1_14_1","volume-title":"Generative adversarial networks: An overview[J]","author":"CRESWELL A","year":"2018","unstructured":"CRESWELL A, WHITE T, DUMOULIN V, ARULKUMARAN K, SENGUPTA B, BHARATH A A. Generative adversarial networks: An overview[J]. IEEE signal processing magazine, 2018, 35(1): 53-65."},{"key":"e_1_3_2_1_15_1","volume-title":"Generative adversarial active learning[J]. arXiv preprint arXiv:170207956","author":"ZHU J-J","year":"2017","unstructured":"ZHU J-J, BENTO J. Generative adversarial active learning[J]. arXiv preprint arXiv:170207956, 2017."},{"key":"e_1_3_2_1_16_1","volume-title":"Adversarial representation active learning[J]. arXiv preprint arXiv:191209720","author":"MOTTAGHI A","year":"2019","unstructured":"MOTTAGHI A, YEUNG S. Adversarial representation active learning[J]. arXiv preprint arXiv:191209720, 2019."},{"key":"e_1_3_2_1_17_1","first-page":"22919","article-title":"Look-Ahead Data Acquisition via Augmentation for Deep Active Learning[J]","volume":"34","author":"KIM Y-Y","year":"2021","unstructured":"KIM Y-Y, SONG K, JANG J, MOON I-C. LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active Learning[J]. Advances in Neural Information Processing Systems, 2021, 34: 22919-22930.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"GONG C WANG D LI M CHANDRA V LIU Q. Keepaugment: A simple information-preserving data augmentation approach[C]\/\/Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2021: 1055-1064.","DOI":"10.1109\/CVPR46437.2021.00111"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2890865"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"YUN S PARK J LEE K SHIN J. Regularizing class-wise predictions via self-knowledge distillation[C]\/\/Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2020: 13876-13885.","DOI":"10.1109\/CVPR42600.2020.01389"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"ZHANG Q-L YANG Y-B. Sa-net: Shuffle attention for deep convolutional neural networks[C]\/\/ICASSP 2021-2021 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP). 2021: 2235-2239.","DOI":"10.1109\/ICASSP39728.2021.9414568"},{"key":"e_1_3_2_1_22_1","volume-title":"Active learning for convolutional neural networks: A core-set approach[J]. arXiv preprint arXiv:170800489","author":"SENER O","year":"2017","unstructured":"SENER O, SAVARESE S. Active learning for convolutional neural networks: A core-set approach[J]. arXiv preprint arXiv:170800489, 2017."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41095-022-0271-y"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"BASTIDAS A A TANG H. Channel attention networks[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2019: 0-0.","DOI":"10.1109\/CVPRW.2019.00117"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01453-z"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"JI M SHIN S HWANG S PARK G MOON I-C. Refine myself by teaching myself: Feature refinement via self-knowledge distillation[C]\/\/Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2021: 10664-10673.","DOI":"10.1109\/CVPR46437.2021.01052"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"ZHANG L SONG J GAO A CHEN J BAO C MA K. Be your own teacher: Improve the performance of convolutional neural networks via self distillation[C]\/\/Proceedings of the IEEE\/CVF International Conference on Computer Vision. 2019: 3713-3722.","DOI":"10.1109\/ICCV.2019.00381"},{"key":"e_1_3_2_1_28_1","volume-title":"Virtual adversarial training: a regularization method for supervised and semi-supervised learning[J]","author":"MIYATO T","year":"2018","unstructured":"MIYATO T, MAEDA S-I, KOYAMA M, ISHII S. Virtual adversarial training: a regularization method for supervised and semi-supervised learning[J]. IEEE transactions on pattern analysis and machine intelligence, 2018, 41(8): 1979-1993."},{"key":"e_1_3_2_1_29_1","volume-title":"When does label smoothing help?[J]. Advances in neural information processing systems","author":"M\u00fcLLER R","year":"2019","unstructured":"M\u00fcLLER R, KORNBLITH S, HINTON G E. When does label smoothing help?[J]. Advances in neural information processing systems, 2019, 32."},{"key":"e_1_3_2_1_30_1","volume-title":"Cifar10-dvs: an event-stream dataset for object classification[J]. Frontiers in neuroscience","author":"LI H","year":"2017","unstructured":"LI H, LIU H, JI X, LI G, SHI L. Cifar10-dvs: an event-stream dataset for object classification[J]. Frontiers in neuroscience, 2017, 11: 309."},{"key":"e_1_3_2_1_31_1","volume-title":"Learning multiple layers of features from tiny images[J]","author":"KRIZHEVSKY A","year":"2009","unstructured":"KRIZHEVSKY A, HINTON G. Learning multiple layers of features from tiny images[J]. 2009."},{"key":"e_1_3_2_1_32_1","volume-title":"Reading digits in natural images with unsupervised feature learning[J]","author":"NETZER Y","year":"2011","unstructured":"NETZER Y, WANG T, COATES A, BISSACCO A, WU B, NG A Y. Reading digits in natural images with unsupervised feature learning[J]. 2011."},{"key":"e_1_3_2_1_33_1","volume-title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms[J]. arXiv preprint arXiv:170807747","author":"XIAO H","year":"2017","unstructured":"XIAO H, RASUL K, VOLLGRAF R. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms[J]. arXiv preprint arXiv:170807747, 2017."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"CARAMALAU R BHATTARAI B KIM T-K. Sequential graph convolutional network for active learning[C]\/\/Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2021: 9583-9592.","DOI":"10.1109\/CVPR46437.2021.00946"}],"event":{"name":"AIPR 2023: 2023 6th International Conference on Artificial Intelligence and Pattern Recognition","acronym":"AIPR 2023","location":"Xiamen China"},"container-title":["2023 6th International Conference on Artificial Intelligence and Pattern Recognition (AIPR)"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3641584.3641615","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3641584.3641615","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:29Z","timestamp":1750295849000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3641584.3641615"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,22]]},"references-count":34,"alternative-id":["10.1145\/3641584.3641615","10.1145\/3641584"],"URL":"https:\/\/doi.org\/10.1145\/3641584.3641615","relation":{},"subject":[],"published":{"date-parts":[[2023,9,22]]},"assertion":[{"value":"2024-06-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}