  Ubiquitous applications of Deep neural networks (DNNs) in different
artificial intelligence systems have led to their adoption in solving
challenging visualization problems in recent years. While sophisticated DNNs
offer an impressive generalization, it is imperative to comprehend the quality,
confidence, robustness, and uncertainty associated with their prediction. A
thorough understanding of these quantities produces actionable insights that
help application scientists make informed decisions. Unfortunately, the
intrinsic design principles of the DNNs cannot beget prediction uncertainty,
necessitating separate formulations for robust uncertainty-aware models for
diverse visualization applications. To that end, this contribution demonstrates
how the prediction uncertainty and sensitivity of DNNs can be estimated
efficiently using various methods and then interactively compared and
contrasted for deep image synthesis tasks. Our inspection suggests that
uncertainty-aware deep visualization models generate illustrations of
informative and superior quality and diversity. Furthermore, prediction
uncertainty improves the robustness and interpretability of deep visualization
models, making them practical and convenient for various scientific domains
that thrive on visual analyses.
