{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T05:42:50Z","timestamp":1730266970201,"version":"3.28.0"},"reference-count":60,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,6,30]],"date-time":"2024-06-30T00:00:00Z","timestamp":1719705600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,6,30]],"date-time":"2024-06-30T00:00:00Z","timestamp":1719705600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,30]]},"DOI":"10.1109\/ijcnn60899.2024.10651555","type":"proceedings-article","created":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T17:35:05Z","timestamp":1725903305000},"page":"1-10","source":"Crossref","is-referenced-by-count":0,"title":["A Multi-objective Perspective Towards Improving Meta-Generalization"],"prefix":"10.1109","author":[{"given":"Weiduo","family":"Liao","sequence":"first","affiliation":[{"name":"City University of Hong Kong,Department of Computer Science"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Wei","sequence":"additional","affiliation":[{"name":"Nanyang Technological University,School of Computer Science and Engineering"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qirui","family":"Sun","sequence":"additional","affiliation":[{"name":"City University of Hong Kong,Department of Computer Science"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingfu","family":"Zhang","sequence":"additional","affiliation":[{"name":"City University of Hong Kong,Department of Computer Science"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hisao","family":"Ishibuchi","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology,Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation,Department of Computer Science and Engineering"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2021.3079209"},{"article-title":"Concept learners for few-shot learning","volume-title":"International Conference on Learning Representations","author":"Cao","key":"ref2"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.5555\/3294996.3295163"},{"key":"ref4","first-page":"3630","article-title":"Matching networks for one shot learning","volume":"29","author":"Vinyals","year":"2016","journal-title":"Advances in Neural Information Processing Systems"},{"article-title":"A simple neural attentive meta-learner","volume-title":"International Conference on Learning Representations","author":"Mishra","key":"ref5"},{"article-title":"Optimization as a model for few-shot learning","volume-title":"International Conference on Learning Representations","author":"Ravi","key":"ref6"},{"article-title":"How to train your MAML","volume-title":"International Conference on Learning Representations","author":"Antoniou","key":"ref7"},{"key":"ref8","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"International Conference on Machine Learning","author":"Finn"},{"article-title":"META-SGD: Learning to learn quickly for few-shot learning","year":"2017","author":"Li","key":"ref9"},{"article-title":"On first-order meta-learning algorithms","year":"2018","author":"Nichol","key":"ref10"},{"article-title":"Meta-learning with latent embedding optimization","volume-title":"International Conference on Learning Representations","author":"Rusu","key":"ref11"},{"article-title":"ES-MAML: simple Hessian-free meta learning","volume-title":"International Conference on Learning Representations","author":"Song","key":"ref12"},{"key":"ref13","first-page":"7045","article-title":"Hierarchically structured meta-learning","volume-title":"International Conference on Machine Learning","author":"Yao"},{"key":"ref14","first-page":"1094","article-title":"Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning","volume-title":"Conference on Robot Learning","author":"Yu"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58607-2_45"},{"key":"ref16","first-page":"10424","article-title":"Learning a universal template for few-shot dataset generalization","volume-title":"International Conference on Machine Learning","author":"Triantafillou"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00834"},{"article-title":"Learning to balance: Bayesian meta-learning for imbalanced and out-of-distribution tasks","volume-title":"International Conference on Learning Representations","author":"Lee","key":"ref18"},{"key":"ref19","first-page":"2927","article-title":"Gradient-based meta-learning with learned layerwise metric and subspace","volume-title":"International Conference on Machine Learning","author":"Lee"},{"key":"ref20","article-title":"Tadam: Task dependent adaptive metric for improved few-shot learning","author":"Oreshkin","year":"2018","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref21","article-title":"Multimodal model-agnostic meta-learning via task-aware modulation","author":"Vuorio","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref22","article-title":"A structured prediction approach for conditional meta-learning","author":"Wang","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref23","first-page":"7343","article-title":"Bayesian model-agnostic meta-learning","author":"Yoon","year":"2018","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref24","first-page":"7115","article-title":"Tapnet: Neural network augmented with task-adaptive projection for fewshot learning","volume-title":"International Conference on Machine Learning","author":"Yoon"},{"article-title":"Automated relational meta-learning","volume-title":"International Conference on Learning Representations","author":"Yao","key":"ref25"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-srw.42"},{"article-title":"Compositional generalization in a deep seq2seq model by separating syntax and semantics","year":"2019","author":"Russin","key":"ref27"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2008.919172"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403230"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00939"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00702"},{"key":"ref32","first-page":"23","article-title":"Task similarity aware meta learning: Theory-inspired improvement on maml","volume-title":"Uncertainty in Artificial Intelligence","author":"Zhou","year":"2021"},{"key":"ref33","article-title":"Multi-task learning as multi-objective optimization","author":"Sener","year":"2018","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref34","first-page":"6597","article-title":"Multi-task learning with user preferences: Gradient descent with controlled ascent in pareto optimization","volume-title":"International Conference on Machine Learning","author":"Mahapatra"},{"article-title":"Multi-Objective Learning to Predict Pareto Fronts Using Hyper-volume Maximization","year":"2021","author":"Deist","key":"ref35"},{"key":"ref36","first-page":"12037","article-title":"Pareto multi-task learning","author":"Lin","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2024.104184"},{"issue":"11","key":"ref38","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"Journal of machine learning research"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.21105\/joss.00861"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/4235.797969"},{"article-title":"The caltech-ucsd birds-200-2011 dataset","year":"2011","author":"Wah","key":"ref42"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.461"},{"key":"ref44","article-title":"Fine-grained visual classification of aircraft","author":"Maji","year":"2013","journal-title":"Computer Vision and Pattern Recognition"},{"year":"2018","key":"ref45","article-title":"2018 fgcvx fungi classification challenge"},{"article-title":"Meta-dataset: A dataset of datasets for learning to learn from few examples","volume-title":"International Conference on Learning Representations","author":"Triantafillou","key":"ref46"},{"issue":"2018","key":"ref47","first-page":"4","article-title":"The quick, draw!-ai experiment","volume":"17","author":"Jongejan","year":"2016","journal-title":"Mount View, CA"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1126\/science.aab3050"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2013.6706807"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1405.0312"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00149"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"Krizhevsky","key":"ref54"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2013.77"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248092"},{"key":"ref57","article-title":"Novel dataset for fine-grained image categorization: Stanford dogs","volume-title":"CVPR Workshop on Fine-Grained Visual Categorization","volume":"2","author":"Khosla"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01450"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00419"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-7687-1_79"}],"event":{"name":"2024 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2024,6,30]]},"location":"Yokohama, Japan","end":{"date-parts":[[2024,7,5]]}},"container-title":["2024 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10649807\/10649898\/10651555.pdf?arnumber=10651555","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T05:12:54Z","timestamp":1726031574000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10651555\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,30]]},"references-count":60,"URL":"https:\/\/doi.org\/10.1109\/ijcnn60899.2024.10651555","relation":{},"subject":[],"published":{"date-parts":[[2024,6,30]]}}}