Uncertainty Aware DeepView: Global Visualization of Unsupervised Quantifiable Uncertainty

While research literature has expanded rapidly regarding explanations in the uncertainty domain, existing methods focus almost exclusively on individual predictions, offering little insight into the distribution of the boundaries that separate confidently modeled regions from unreliable ones. We propose Uncertainty Aware Deepview, the first globally faithful uncertainty visualization technique that is applicable to any type of model, and comes with a robustness estimate. Uncertainty Aware DeepView removes the requirement of an uncertainty output of the model and combines two concepts for the determination of unsupervised quantifiable uncertaintes: An adversarial score estimating the local instability of a sample, and an out-of-distribution score quantifying the distributional anomality of a sample. We demonstrate Uncertainty Aware Deepview's ability to accurately identify out-of-distribution data as well as adversarial examples on different models including Autoencoders, CNNs, Resnets and Vision Transformers on multiple benchmark datasets.