  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.
