论文标题
为深度学习图像分类器提供错误检测，可使用自我解释
Providing Error Detection for Deep Learning Image Classifiers Using Self-Explainability
论文作者
论文摘要
本文为执行自我检测的图像分类问题提出了一个可自我解释的深度学习（SE-DL）系统。自我检测是改善DL系统安全操作的关键，尤其是在诸如汽车系统之类的安全性应用程序中。 SE-DL系统既输出了类预测，又输出了该预测的解释，该预测提供了有关系统如何对人类进行预测的见解。此外，我们利用所提出的SE-DL系统的解释来检测系统的潜在类预测错误。提出的SE-DL系统使用一组概念来生成解释。这些概念是与该图像的高级类别相关的每个输入图像中的人为理解的低级图像特征。我们提出了一种概念选择方法，用于评分所有概念，并根据其对所提出的SE-DL系统的错误检测性能的贡献选择其中的子集。最后，我们使用建议的SE-DL系统提出了不同的错误检测方案，以将它们与没有任何SE-DL系统的错误检测方案进行比较。
This paper proposes a self-explainable Deep Learning (SE-DL) system for an image classification problem that performs self-error detection. The self-error detection is key to improving the DL system's safe operation, especially in safety-critical applications such as automotive systems. A SE-DL system outputs both the class prediction and an explanation for that prediction, which provides insight into how the system makes its predictions for humans. Additionally, we leverage the explanation of the proposed SE-DL system to detect potential class prediction errors of the system. The proposed SE-DL system uses a set of concepts to generate the explanation. The concepts are human-understandable lower-level image features in each input image relevant to the higher-level class of that image. We present a concept selection methodology for scoring all concepts and selecting a subset of them based on their contribution to the error detection performance of the proposed SE-DL system. Finally, we present different error detection schemes using the proposed SE-DL system to compare them against an error detection scheme without any SE-DL system.
