Visual Analysis of Prediction Uncertainty in Neural Networks for Deep Image Synthesis

TL;DR

This paper explores methods to estimate and compare prediction uncertainty in neural networks used for deep image synthesis, enhancing their robustness, interpretability, and quality for scientific visualization.

Contribution

It introduces efficient techniques for estimating and contrasting uncertainty in DNNs, improving deep visualization models' robustness and interpretability.

Findings

Uncertainty-aware models produce higher quality and more diverse visualizations.

Prediction uncertainty enhances model robustness and interpretability.

Methods enable interactive comparison of uncertainty estimates in deep image synthesis.

Abstract

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.