{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T06:59:59Z","timestamp":1770706799233,"version":"3.49.0"},"reference-count":16,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,1,2]],"date-time":"2021-01-02T00:00:00Z","timestamp":1609545600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,2]],"date-time":"2021-01-02T00:00:00Z","timestamp":1609545600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"the Strategic Priority Research Program of the Chinese Academy of Sciences","award":["Grant No. XDA19080102"],"award-info":[{"award-number":["Grant No. XDA19080102"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Vis"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s12650-020-00704-4","type":"journal-article","created":{"date-parts":[[2021,1,2]],"date-time":"2021-01-02T00:06:01Z","timestamp":1609545961000},"page":"301-315","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Visual analysis of meteorological satellite data via model-agnostic meta-learning"],"prefix":"10.1007","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7400-515X","authenticated-orcid":false,"given":"Shiyu","family":"Cheng","sequence":"first","affiliation":[]},{"given":"Hanwei","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Guihua","family":"Shan","sequence":"additional","affiliation":[]},{"given":"Beifang","family":"Niu","sequence":"additional","affiliation":[]},{"given":"Weihua","family":"Bai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,2]]},"reference":[{"key":"704_CR1","unstructured":"Antoniou A, Edwards H, Storkey A (2018) How to train your MAML. arXiv e-prints, p. arXiv:1810.09502"},{"key":"704_CR2","doi-asserted-by":"publisher","first-page":"1817","DOI":"10.5194\/amt-7-1817-2014","volume":"7","author":"W Bai","year":"2014","unstructured":"Bai W, Sun Y, Du Q, Yang G, Yang Z, Zhang P, Bi Y, Wang X, Cheng C, Han Y (2014) An introduction to the fy3 gnos instrument and mountain-top tests. Atmos Meas Tech 7:1817\u20131823. https:\/\/doi.org\/10.5194\/amt-7-1817-2014","journal-title":"Atmos Meas Tech"},{"key":"704_CR3","doi-asserted-by":"publisher","first-page":"01","DOI":"10.1007\/s12650-018-00545-2","volume":"22","author":"S Cheng","year":"2019","unstructured":"Cheng S, Shan G, Liu J, Gao Y, Wei P, Bai W, Zhao D (2019) Occvis: a visual analytics system for occultation data. J Vis 22:01. https:\/\/doi.org\/10.1007\/s12650-018-00545-2","journal-title":"J Vis"},{"key":"704_CR4","unstructured":"Finn C, Abbeel P, Levine S (2017) Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. arXiv e-prints, p. arXiv:1703.03400"},{"key":"704_CR5","doi-asserted-by":"publisher","unstructured":"Hong F, Chen S, Guo H, Yuan X, Huang J, Zhang Y (2017) Visual exploration of ionosphere disturbances for earthquake research. pp. 1\u20138, 11. https:\/\/doi.org\/10.1145\/3139295.3139301","DOI":"10.1145\/3139295.3139301"},{"key":"704_CR6","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2017.2744718","author":"M Kahng","year":"2017","unstructured":"Kahng M, Andrews P, Kalro A, Chau DH (2017) Activis: Visual exploration of industry-scale deep neural network models. IEEE Trans Vis Comput Gr. https:\/\/doi.org\/10.1109\/TVCG.2017.2744718","journal-title":"IEEE Trans Vis Comput Gr"},{"key":"704_CR7","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2018.2864500","author":"M Kahng","year":"2018","unstructured":"Kahng M, Thorat N, Chau DH, Viegas F, Wattenberg M (2018) Gan lab: Understanding complex deep generative models using interactive visual experimentation. IEEE Trans Vis Comput Gr. https:\/\/doi.org\/10.1109\/TVCG.2018.2864500","journal-title":"IEEE Trans Vis Comput Gr"},{"key":"704_CR8","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2017.2744938","author":"M Liu","year":"2017","unstructured":"Liu M, Shi J, Cao K, Zhu J, Liu S (2017) Analyzing the training processes of deep generative models. IEEE Trans Vis Comput Gr. https:\/\/doi.org\/10.1109\/TVCG.2017.2744938","journal-title":"IEEE Trans Vis Comput Gr"},{"key":"704_CR9","doi-asserted-by":"publisher","first-page":"04","DOI":"10.1109\/TVCG.2016.2598831","volume":"23","author":"M Liu","year":"2016","unstructured":"Liu M, Shi J, Li Z, Li C, Zhu J, Liu S (2016) Towards better analysis of deep convolutional neural networks. IEEE Trans Vis Comput Gr 23:04. https:\/\/doi.org\/10.1109\/TVCG.2016.2598831","journal-title":"IEEE Trans Vis Comput Gr"},{"key":"704_CR10","unstructured":"Nichol A, Achiam J, Schulman J (2018) On First-Order Meta-Learning Algorithms. arXiv e-prints, p. arXiv:1803.02999"},{"key":"704_CR11","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.1016\/j.asr.2010.01.018","volume":"45","author":"C Noll","year":"2010","unstructured":"Noll C (2010) The crustal dynamics data information system: a resource to support scientific analysis using space geodesy. Adv Space Res 45:1421\u20131440. https:\/\/doi.org\/10.1016\/j.asr.2010.01.018","journal-title":"Adv Space Res"},{"key":"704_CR12","doi-asserted-by":"publisher","first-page":"01","DOI":"10.1109\/TVCG.2017.2744358","volume":"24","author":"N Pezzotti","year":"2018","unstructured":"Pezzotti N, H\u00f6llt T, Gemert J, Lelieveldt B, Eisemann E, Vilanova A (2018) Deepeyes: progressive visual analytics for designing deep neural networks. IEEE Trans Vis Comput Gr 24:01. https:\/\/doi.org\/10.1109\/TVCG.2017.2744358","journal-title":"IEEE Trans Vis Comput Gr"},{"key":"704_CR13","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2018.2864504","author":"J Wang","year":"2018","unstructured":"Wang J, Gou L, Shen H-W, Yang H (2018) Dqnviz: a visual analytics approach to understand deep q-networks. IEEE Trans Vis Comput Gr. https:\/\/doi.org\/10.1109\/TVCG.2018.2864504","journal-title":"IEEE Trans Vis Comput Gr"},{"key":"704_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2018.2816223","author":"J Wang","year":"2018","unstructured":"Wang J, Gou L, Yang H, Shen H-W (2018) Ganviz: a visual analytics approach to understand the adversarial game. IEEE Trans Vis Comput Gr. https:\/\/doi.org\/10.1109\/TVCG.2018.2816223","journal-title":"IEEE Trans Vis Comput Gr"},{"key":"704_CR15","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2016.2598830","author":"J Wang","year":"2016","unstructured":"Wang J, Liu X, Shen H-W, Lin G (2016) Multi-resolution climate ensemble parameter analysis with nested parallel coordinates plots. IEEE Trans Vis Comput Gr. https:\/\/doi.org\/10.1109\/TVCG.2016.2598830","journal-title":"IEEE Trans Vis Comput Gr"},{"key":"704_CR16","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2017.2744878","author":"K Wongsuphasawat","year":"2017","unstructured":"Wongsuphasawat K, Smilkov D, Wexler J, Wilson J, Mane D, Fritz D, Krishnan D, Viegas F, Wattenberg M (2017) Visualizing dataflow graphs of deep learning models in tensorflow. IEEE Trans Vis Comput Gr. https:\/\/doi.org\/10.1109\/TVCG.2017.2744878","journal-title":"IEEE Trans Vis Comput Gr"}],"container-title":["Journal of Visualization"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12650-020-00704-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12650-020-00704-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12650-020-00704-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T03:08:44Z","timestamp":1616123324000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12650-020-00704-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,2]]},"references-count":16,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["704"],"URL":"https:\/\/doi.org\/10.1007\/s12650-020-00704-4","relation":{},"ISSN":["1343-8875","1875-8975"],"issn-type":[{"value":"1343-8875","type":"print"},{"value":"1875-8975","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,2]]},"assertion":[{"value":"29 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}