2021
DOI: 10.1109/mcg.2021.3097555
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STSRNet: Deep Joint Space–Time Super-Resolution for Vector Field Visualization

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Cited by 14 publications
(14 citation statements)
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“…This makes unsupervised learning, particularly self-supervised learning, a suitable candidate for accomplishing such tasks. Thus, investigating the underexplored self-supervised learning solutions for making predictions or recommendations will certainly boost [2], [27], [45], [46], [47], [49], [50] [12], [63] [35], [57] [11], [15] [24] + [51], [53], [54], [55] , [56], [76], [84] [70], [158] [71], [85] [31], [40] [126], [131], [156] + , [160], [162] [159]…”
Section: Discussionmentioning
confidence: 99%
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“…This makes unsupervised learning, particularly self-supervised learning, a suitable candidate for accomplishing such tasks. Thus, investigating the underexplored self-supervised learning solutions for making predictions or recommendations will certainly boost [2], [27], [45], [46], [47], [49], [50] [12], [63] [35], [57] [11], [15] [24] + [51], [53], [54], [55] , [56], [76], [84] [70], [158] [71], [85] [31], [40] [126], [131], [156] + , [160], [162] [159]…”
Section: Discussionmentioning
confidence: 99%
“…We also refer to their research tasks when categorizing the surveyed papers in the respective tables according to learning type, network architecture, loss function, and evaluation metric. The description [51] TSR-TVD TVCG Han and Wang [50] SSR-TVD TVCG Han et al [55] STNet TVCG Wurster et al [167] arXiv Guo et al [47] SSR-VFD PVIS Jakob et al [76] TVCG Sahoo and Berger [126] IA-VFS EVIS An et al [2] STSRNet CG&A Han and Wang [53] TSR-VFD C&G Xie et al [168] tempoGAN TOG Werhahn et al [162] CGIT Wang et al [156] DeepOrganNet TVCG Lu et al [109] neurcomp CGF Weiss et al [160] fV-SRN arXiv Shi et al [131] GNN-Surrogate TVCG Han and Wang [54] VCNet VI Liu et al [106] JOV Han et al [49] CG&A Gu et al [45] VFR-UFD CG&A Han et al [56] V2V TVCG Gu et al [46] Scalar2Vec PVIS Kim et al [84] Deep Fluids CGF Chu et al [27] TOG Wiewel et al [163] LSP CGF Wiewel et al [164] LSS CGF Berger et al [12] TVCG Hong et al [70] DNN-VolVis PVIS He et al [63] InSituNet TVCG Weiss et al [159] TVCG Weiss et al [161] TVCG Weiss and Navab [158] DeepDVR arXiv He et al [62] CECAV-DNN VI Tkachev et al [143] TVCG Hong et al [71] PVIS Kim and Günther [85] CGF Han et al [57] arXiv Yang et al [169] JOV Shi and Tao [130] TIST Engel and Ropinski …”
Section: Dl4scivis Workmentioning
confidence: 99%
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