{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T18:16:54Z","timestamp":1730225814225,"version":"3.28.0"},"reference-count":36,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T00:00:00Z","timestamp":1695600000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T00:00:00Z","timestamp":1695600000000},"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":[[2023,9,25]]},"DOI":"10.1109\/hpec58863.2023.10363448","type":"proceedings-article","created":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T14:39:57Z","timestamp":1703515197000},"page":"1-9","source":"Crossref","is-referenced-by-count":0,"title":["Meta-Learning and Self-Supervised Pretraining for Storm Event Imagery Translation"],"prefix":"10.1109","author":[{"given":"Ileana","family":"Rugina","sequence":"first","affiliation":[{"name":"MIT EECS"}]},{"given":"Rumen","family":"Dangovski","sequence":"additional","affiliation":[{"name":"MIT EECS"}]},{"given":"Mark","family":"Veillette","sequence":"additional","affiliation":[{"name":"MIT Lincoln Lab"}]},{"given":"Pooya","family":"Khorrami","sequence":"additional","affiliation":[{"name":"MIT Lincoln Lab"}]},{"given":"Brian","family":"Cheung","sequence":"additional","affiliation":[{"name":"MIT CSAIL &#x0026; BCS"}]},{"given":"Olga","family":"Simek","sequence":"additional","affiliation":[{"name":"MIT Lincoln Lab"}]},{"given":"Marin","family":"Solja\u010di\u0107","sequence":"additional","affiliation":[{"name":"MIT Physics"}]}],"member":"263","reference":[{"volume-title":"learn2learn: A library for Meta-Learning research","year":"2020","author":"Arnold","key":"ref1"},{"key":"ref2","article-title":"A cookbook of self-supervised learning","author":"Balestriero","year":"2023","journal-title":"arXiv preprint"},{"key":"ref3","article-title":"Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Barbu","year":"2019"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/iccv48922.2021.00950"},{"key":"ref5","article-title":"FIGR: few-shot image generation with reptile","volume":"abs\/1901.02199","author":"Clouatre","year":"2019","journal-title":"CoRR"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00331"},{"key":"ref8","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research","author":"Finn","year":"2017"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/hpec55821.2022.9991948"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"journal-title":"Denoising diffusion probabilistic models","year":"2020","author":"Ho","key":"ref11"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2021.3079209"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3079209"},{"journal-title":"A method for stochastic optimization","year":"2017","author":"Kingma","key":"ref14"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1126\/science.aab3050"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.cobeha.2019.04.007"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1126\/science.adi2336"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.3031549"},{"journal-title":"On first-order meta-learning algorithms","year":"2018","author":"Nichol","key":"ref19"},{"key":"ref20","article-title":"Representation learning with contrastive predictive coding","author":"Van Den Oord","year":"2018","journal-title":"arXiv preprint"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3592979.3593412"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1264"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03854-z"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.bdr.2020.100178"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/HPEC.2018.8547629"},{"journal-title":"Meta-learning and self-supervised pretraining for real world image translation","year":"2021","author":"Rugina","key":"ref26"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1175\/bams-d-15-00230.1"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2020.0097"},{"key":"ref29","article-title":"Meta-GAN for few-shot image generation","volume-title":"ICLR Workshop on Deep Generative Models for Highly Structured Data","author":"Sridhar","year":"2022"},{"key":"ref30","article-title":"Meta-dataset: A dataset of datasets for learning to learn from few examples","volume":"abs\/1903.03096","author":"Triantafillou","year":"2019","journal-title":"CoRR"},{"key":"ref31","first-page":"22009","article-title":"Sevir: A storm event imagery dataset for deep learning applications in radar and satellite meteorology","volume-title":"Advances in Neural Information Processing Systems","volume":"33","author":"Veillette","year":"2020"},{"journal-title":"Matching networks for one shot learning","year":"2017","author":"Vinyals","key":"ref32"},{"journal-title":"Pretraining is all you need for image-to-image translation","year":"2022","author":"Wang","key":"ref33"},{"key":"ref34","article-title":"Consistency regularization for generative adversarial networks","volume":"abs\/1910.12027","author":"Zhang","year":"2019","journal-title":"CoRR"},{"journal-title":"Differentiable augmentation for data-efficient gan training","year":"2020","author":"Zhao","key":"ref35"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17317"}],"event":{"name":"2023 IEEE High Performance Extreme Computing Conference (HPEC)","start":{"date-parts":[[2023,9,25]]},"location":"Boston, MA, USA","end":{"date-parts":[[2023,9,29]]}},"container-title":["2023 IEEE High Performance Extreme Computing Conference (HPEC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10363430\/10363422\/10363448.pdf?arnumber=10363448","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T15:45:47Z","timestamp":1705074347000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10363448\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,25]]},"references-count":36,"URL":"https:\/\/doi.org\/10.1109\/hpec58863.2023.10363448","relation":{},"subject":[],"published":{"date-parts":[[2023,9,25]]}}}