{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T19:42:44Z","timestamp":1730230964434,"version":"3.28.0"},"reference-count":21,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,6,4]],"date-time":"2023-06-04T00:00:00Z","timestamp":1685836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,6,4]],"date-time":"2023-06-04T00:00:00Z","timestamp":1685836800000},"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":[],"published-print":{"date-parts":[[2023,6,4]]},"DOI":"10.1109\/icassp49357.2023.10096594","type":"proceedings-article","created":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T17:28:30Z","timestamp":1683307710000},"page":"1-5","source":"Crossref","is-referenced-by-count":0,"title":["Dual Meta Calibration Mix for Improving Generalization in Meta-Learning"],"prefix":"10.1109","author":[{"given":"Ze-Yu","family":"Mi","sequence":"first","affiliation":[{"name":"Nanjing University,State Key Laboratory for Novel Software Technology,Nanjing,China,210023"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Bin","family":"Yang","sequence":"additional","affiliation":[{"name":"Nanjing University,State Key Laboratory for Novel Software Technology,Nanjing,China,210023"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"volume-title":"Learning to learn","year":"2012","author":"Thrun","key":"ref1"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00851"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.01065"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1112"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1542"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05318-5_2"},{"key":"ref7","first-page":"5705","article-title":"Meta-learning requires meta-augmentation","volume":"33","author":"Rajendran","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"article-title":"Metalearning with fewer tasks through task interpolation","year":"2021","author":"Yao","key":"ref8"},{"article-title":"Meta dropout: Learning to perturb latent features for generalization","year":"2020","author":"Lee","key":"ref9"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-7687-1_79"},{"key":"ref11","first-page":"8152","article-title":"Data augmentation for metalearning","volume-title":"International Conference on Machine Learning","author":"Ni"},{"key":"ref12","first-page":"11887","article-title":"Improving generalization in meta-learning via task augmentation","volume-title":"International Conference on Machine Learning","author":"Yao"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref14","first-page":"6438","article-title":"Manifold mixup: Better representations by interpolating hidden states","volume-title":"International Conference on Machine Learning","author":"Verma"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref16","article-title":"Matching networks for one shot learning","volume":"29","author":"Vinyals","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363547"},{"year":"2016","key":"ref18","article-title":"Dermnet dataset"},{"article-title":"Concept learners for few-shot learning","year":"2020","author":"Cao","key":"ref19"},{"article-title":"Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm","year":"2017","author":"Finn","key":"ref20"},{"key":"ref21","article-title":"Prototypical networks for few-shot learning","volume":"30","author":"Snell","year":"2017","journal-title":"Advances in neural information processing systems"}],"event":{"name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","start":{"date-parts":[[2023,6,4]]},"location":"Rhodes Island, Greece","end":{"date-parts":[[2023,6,10]]}},"container-title":["ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10094559\/10094560\/10096594.pdf?arnumber=10096594","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T02:02:23Z","timestamp":1705024943000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10096594\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,4]]},"references-count":21,"URL":"https:\/\/doi.org\/10.1109\/icassp49357.2023.10096594","relation":{},"subject":[],"published":{"date-parts":[[2023,6,4]]}}}