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Found 5 papers, 2 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".
Using Fictitious Class Representations to Boost Discriminative Zero-Shot Learners
Focusing on discriminative zero-shot learning, in this work we introduce a novel mechanism that dynamically augments during training the set of seen classes to produce additional fictitious classes.
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.
