IDLat: An Importance-Driven Latent Generation Method for Scientific Data
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DOI: 10.1109/tvcg.2022.3209419
Abstract: Deep learning based latent representations have been widely used for numerous scientific visualization applications such as isosurface similarity analysis, volume rendering, flow field synthesis, and data reduction, just to name a few. However, existing latent… read more here.
Keywords: latent generation; importance; scientific data; importance driven ... See more keywords