2.2k citations · 5.9k across the 41 of their papers we have counts for
41 papers
A framework for benchmarking class-out-of-distribution detection and its application to ImageNet
Ido Galil, Mohammed Dabbah, Ran El-Yaniv
When deployed for risk-sensitive tasks, deep neural networks must be able to detect instances with labels from outside the distribution for which they were trained. In this paper w…
What Can We Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers
Ido Galil, Mohammed Dabbah, Ran El-Yaniv
When deployed for risk-sensitive tasks, deep neural networks must include an uncertainty estimation mechanism. Here we examine the relationship between deep architectures and their…
Window-Based Distribution Shift Detection for Deep Neural Networks
Guy Bar-Shalom, Yonatan Geifman, Ran El-Yaniv
To deploy and operate deep neural models in production, the quality of their predictions, which might be contaminated benignly or manipulated maliciously by input distributional de…
Which models are innately best at uncertainty estimation?
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 App…
TransBoost: Improving the Best ImageNet Performance using Deep Transduction
Omer Belhasin, Guy Bar-Shalom, Ran El-Yaniv
This paper deals with deep transductive learning, and proposes TransBoost as a procedure for fine-tuning any deep neural model to improve its performance on any (unlabeled) test se…
Using Fictitious Class Representations to Boost Discriminative Zero-Shot Learners
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 f…