IDLat: An Importance-Driven Latent Generation Method for Scientific Data

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 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 facilitate domain-interest-guided scientific data visualization and analysis. We utilize spatial importance maps to represent various scientific interests and take them as the input to a feature transformation network to guide latent generation. We further reduced the latent size by a lossless entropy encoding algorithm trained together with the autoencoder, improving the storage and memory efficiency. We qualitatively and quantitatively evaluate the effectiveness and efficiency of latent representations generated by our method with data from multiple scientific visualization applications. First, to incorporate domain by autoencoders have attracted great attentions of researchers in recent interests into latent representations, we extend the basic autoencoder years. Latent representations have been successfully demonstrated to with a feature transformation network that takes domain interest as an retain essential information in the original data, and can be used for input to guide the mapping from scientific data to latent representations. Every been applied to multivariate volumetric data [28], streamlines and element in the importance map is a real value indicating how vital this stream surfaces [18], isosurfaces [12], and particles [25]. The importance Although latent representations for large-scale scientific data have values can be derived mathematically based on the domain or been used extensively, there are still several challenges. First, domain heuristically based on distances, distributions, locations, etc., depending scientists have diverse interests in different data portions, but latent on the underlying scientific applications.