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.

利用不同方法高效估计深度神经网络的预测不确定性和敏感性，并将其互动比较和对比，以应对可靠的不确定性感知模型在深度图像合成任务中产生信息丰富且质量多样的插图，进一步提高深度可视化模型的稳健性和可解释性，使其在以视觉分析为基础的各个科学领域得以实际应用和方便使用。