IDLat: An Importance-Driven Latent Generation Method for Scientific Data. (arXiv:2208.03345v1 [cs.LG])

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 representations are mostly generated from raw data in
an unsupervised manner, which makes it difficult to incorporate domain interest
to control the size of the latent representations and the quality of the
reconstructed data. In this paper, we present a novel importance-driven latent
representation to …

arxiv data generation importance lg