Deep neural networks (DNNs) have shown to perform very well on large scale
object recognition problems and lead to widespread use for real-world
applications, including situations where DNN are implemented as "black boxes".
A promising approach to secure their use is to accept decisions that are likely
to be correct while discarding the others. In this work, we propose DOCTOR, a
simple method that aims to identify whether the prediction of a DNN classifier
should (or should not) be trusted so that, consequently, it would be possible
to accept it or to reject it. Two scenarios are investigated: Totally Black Box
(TBB) where only the soft-predictions are available and Partially Black Box
(PBB) where gradient-propagation to perform input pre-processing is allowed.
Empirically, we show that DOCTOR outperforms all state-of-the-art methods on
various well-known images and sentiment analysis datasets. In particular, we
observe a reduction of up to $4\%$ of the false rejection rate (FRR) in the PBB
scenario. DOCTOR can be applied to any pre-trained model, it does not require
prior information about the underlying dataset and is as simple as the simplest
available methods in the literature.

该研究提出了一种名为 DOCTOR 的简单方法，用于确定是否应信任 DNN 分类器的预测结果，以便能够接受或拒绝该结果。研究表明，DOCTOR 在各种著名图像和情感分析数据集上均优于现有方法。

DOCTOR: 检测分类错误的简易方法

DOCTOR: A Simple Method for Detecting Misclassification Errors

Ensemble learning is a methodology that integrates multiple DNN learners for
improving prediction performance of individual learners. Diversity is greater
when the errors of the ensemble prediction is more uniformly distributed.
Greater diversity is highly correlated with the increase in ensemble accuracy.
Another attractive property of diversity optimized ensemble learning is its
robustness against deception: an adversarial perturbation attack can mislead
one DNN model to misclassify but may not fool other ensemble DNN members
consistently. In this paper we first give an overview of the concept of
ensemble diversity and examine the three types of ensemble diversity in the
context of DNN classifiers. We then describe a set of ensemble diversity
measures, a suite of algorithms for creating diversity ensembles and for
performing ensemble consensus (voted or learned) for generating high accuracy
ensemble output by strategically combining outputs of individual members. This
paper concludes with a discussion on a set of open issues in quantifying
ensemble diversity for robust deep learning.

本文阐述了集成学习的一些基本概念，研究了如何优化集成学习的多样性来对抗敌对攻击，并提供了一些算法来学习如何生成高精度的集成预测。