{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T17:57:42Z","timestamp":1770227862334,"version":"3.49.0"},"reference-count":61,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/tpami.2022.3213755","type":"journal-article","created":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T19:32:44Z","timestamp":1665516764000},"page":"1-18","source":"Crossref","is-referenced-by-count":6,"title":["Defensive Few-shot Learning"],"prefix":"10.1109","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0935-7124","authenticated-orcid":false,"given":"Wenbin","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0961-0441","authenticated-orcid":false,"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computing and Information Technology, University of Wollongong, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4012-3796","authenticated-orcid":false,"given":"Xingxing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Tsinghua University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7091-0702","authenticated-orcid":false,"given":"Lei","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8504-455X","authenticated-orcid":false,"given":"Jing","family":"Huo","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2488-1813","authenticated-orcid":false,"given":"Yang","family":"Gao","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4516-9729","authenticated-orcid":false,"given":"Jiebo","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Rochester, America"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"ref2","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Informat. Process. Syst.","author":"Krizhevsky"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.10.013"},{"key":"ref5","first-page":"1","article-title":"Explaining and harnessing adversarial examples","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Goodfellow"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1049\/cit2.12028"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.485"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.06083"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858821"},{"key":"ref10","first-page":"274","article-title":"Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"80","author":"Athalye"},{"key":"ref11","article-title":"Adversarial logit pairing","author":"Kannan"},{"key":"ref12","first-page":"1","article-title":"Structured adversarial attack: Towards general implementation and better interpretability","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xu"},{"key":"ref13","article-title":"Robustness may be at odds with accuracy","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Tsipras"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2986319"},{"key":"ref15","article-title":"Revisiting few-shot learning for facial expression recognition","author":"Ciubotaru","year":"2019"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICSIP49896.2020.9339429"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00389"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3060551"},{"key":"ref19","first-page":"5014","article-title":"Adversarially robust generalization requires more data","volume-title":"Proc. Adv. Neural Informat. Process. Syst.","author":"Schmidt"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00284"},{"key":"ref21","first-page":"7472","article-title":"Theoretically principled trade-off between robustness and accuracy","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"97","author":"Zhang"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2006.79"},{"key":"ref23","first-page":"3630","article-title":"Matching networks for one shot learning","volume-title":"Proc. Adv. Neural Informat. Process. Syst.","author":"Vinyals"},{"key":"ref24","first-page":"232","article-title":"Infinite mixture prototypes for few-shot learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Allen"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00743"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01199"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013379"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00888"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2994749"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/409"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/100"},{"key":"ref32","article-title":"Learning from very few samples: A survey","author":"Lu","year":"2020"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3018506"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_35"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00870"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2023.3312125\/mm1"},{"key":"ref37","first-page":"4077","article-title":"Prototypical networks for few-shot learning","volume-title":"Proc. Adv. Neural Informat. Process. Syst.","author":"Snell"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018642"},{"key":"ref40","first-page":"4005","article-title":"Cross attention network for few-shot classification","volume-title":"Proc. Adv. Neural Informat. Process. Syst.","author":"Hou"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01222"},{"key":"ref42","first-page":"1","article-title":"IEPT: Instance-level and episode-level pretext tasks for few-shot learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhang"},{"key":"ref43","first-page":"1","article-title":"Cascade adversarial machine learning regularized with a unified embedding","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Na"},{"key":"ref44","first-page":"1","article-title":"Improving the generalization of adversarial training with domain adaptation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Song"},{"key":"ref45","first-page":"1","article-title":"Intriguing properties of neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Szegedy"},{"key":"ref46","first-page":"1","article-title":"Adversarial machine learning at scale","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kurakin"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/0024-3795(82)90112-4"},{"key":"ref49","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kingma"},{"key":"ref50","first-page":"1","article-title":"Meta-learning for semi-supervised few-shot classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ren"},{"key":"ref51","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref52","article-title":"Novel dataset for fine-grained image categorization: Stanford dogs","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshop","author":"Khosla"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2013.77"},{"key":"ref54","article-title":"The caltech-ucsd birds-200\u20132011 dataset","author":"Wah","year":"2011"},{"key":"ref55","first-page":"1","article-title":"Optimization as a model for few-shot learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ravi"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00049"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref58","first-page":"4080","article-title":"Prototypical networks for few-shot learning","volume-title":"Proc. Adv. Neural Informat. Process. Syst.","author":"Snell"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.282"},{"key":"ref60","first-page":"1","article-title":"A closer look at few-shot classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chen"},{"key":"ref61","first-page":"1","article-title":"Meta-dataset: A dataset of datasets for learning to learn from few examples","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Triantafillou"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/4359286\/09916072.pdf?arnumber=9916072","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T22:56:22Z","timestamp":1705964182000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9916072\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":61,"URL":"https:\/\/doi.org\/10.1109\/tpami.2022.3213755","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}