1 code implementation • ICLR 2023 • Ido Galil, Mohammed Dabbah, Ran El-Yaniv
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.
1 code implementation • 23 Feb 2023 • Ido Galil, Mohammed Dabbah, Ran El-Yaniv
Here we examine the relationship between deep architectures and their respective training regimes, with their corresponding selective prediction and uncertainty estimation performance.
no code implementations • 5 Jun 2022 • Ido Galil, Mohammed Dabbah, Ran El-Yaniv
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".
no code implementations • 26 Nov 2021 • Mohammed Dabbah, Ran El-Yaniv
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.
no code implementations • 29 Sep 2021 • Ido Galil, Mohammed Dabbah, Ran El-Yaniv
Moreover, we consider some of the most popular estimation performance metrics previously proposed including AUROC, ECE, AURC, and coverage for selective accuracy constraint.