Decision processes of computer vision models - especially deep neural
networks - are opaque in nature, meaning that these decisions cannot be
understood by humans. Thus, over the last years, many methods to provide
human-understandable explanations have been proposed. For image classification,
the most common group are saliency methods, which provide (super-)pixelwise
feature attribution scores for input images. But their evaluation still poses a
problem, as their results cannot be simply compared to the unknown ground
truth. To overcome this, a slew of different proxy metrics have been defined,
which are - as the explainability methods themselves - often built on intuition
and thus, are possibly unreliable. In this paper, new evaluation metrics for
saliency methods are developed and common saliency methods are benchmarked on
ImageNet. In addition, a scheme for reliability evaluation of such metrics is
proposed that is based on concepts from psychometric testing. The used code can
be found at
this https URL .

计算机视觉模型的决策过程（尤其是深度神经网络）的不透明性意味着这些决策无法被人类理解。因此，在过去几年中，已经提出了许多提供人理解解释的方法。本文针对图像分类开发了新的评估指标，并对常见的显著性方法在 ImageNet 上进行了基准测试。此外，还提出了一种基于心理测评概念的可靠性评估方案。

图像解释的分类评估指标：构建可靠的可解释性人工智能评估

Classification Metrics for Image Explanations: Towards Building Reliable  XAI-Evaluations

The reliability of deep learning accelerators (DLAs) used in autonomous
driving systems has significant impact on the system safety. However, the DLA
reliability is usually evaluated with low-level metrics like mean square errors
of the output which remains rather different from the high-level metrics like
total distance traveled before failure in autonomous driving. As a result, the
high-level reliability metrics evaluated at the post-silicon stage may still
lead to DLA design revision and result in expensive reliable DLA design
iterations targeting at autonomous driving. To address the problem, we proposed
a DLA-in-loop reliability evaluation platform to enable system reliability
evaluation at the early DLA design stage.

本文提出了一个 DLA-in-loop 可靠性评估平台，用于解决自动驾驶中深度学习加速器（DLAs）可靠性评估中高级度量与底层度量不一致的问题。