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Found 5 papers, 3 papers with code
A framework for benchmarking class-out-of-distribution detection and its application to ImageNet
In this paper we present a novel framework to benchmark the ability of image classifiers to detect class-out-of-distribution instances (i. e., instances whose true labels do not appear in the training distribution) at various levels of detection difficulty.
What Can We Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers
Here we examine the relationship between deep architectures and their respective training regimes, with their corresponding selective prediction and uncertainty estimation performance.
Which models are innately best at uncertainty estimation?
Due to the comprehensive nature of this paper, it has been updated and split into two separate papers: "A Framework For Benchmarking Class-out-of-distribution Detection And Its Application To ImageNet" and "What Can We Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers".
Disrupting Deep Uncertainty Estimation Without Harming Accuracy
In this paper we present a novel and simple attack, which unlike adversarial attacks, does not cause incorrect predictions but instead cripples the network's capacity for uncertainty estimation.
How to measure deep uncertainty estimation performance and which models are naturally better at providing it
Moreover, we consider some of the most popular estimation performance metrics previously proposed including AUROC, ECE, AURC, and coverage for selective accuracy constraint.
