Saliency methods have emerged as a popular tool to highlight features in an
input deemed relevant for the prediction of a learned model. Several saliency
methods have been proposed, often guided by visual appeal on image data. In
this work, we propose an actionable methodology to evaluate what kinds of
explanations a given method can and cannot provide. We find that reliance,
solely, on visual assessment can be misleading. Through extensive experiments
we show that some existing saliency methods are independent both of the model
and of the data generating process. Consequently, methods that fail the
proposed tests are inadequate for tasks that are sensitive to either data or
model, such as, finding outliers in the data, explaining the relationship
between inputs and outputs that the model learned, and debugging the model. We
interpret our findings through an analogy with edge detection in images, a
technique that requires neither training data nor model. Theory in the case of
a linear model and a single-layer convolutional neural network supports our
experimental findings.

本文对几种显著性方法进行实验和理论探索，提出用可行的方法来评估一种方法所能提供和不能提供的解释类型。实验证明，有些现有的显著性方法具有独立性，这些方法不受模型和数据生成过程的影响，因此，那些未通过所提出测试的方法，对于对数据或模型敏感的任务（ 如在数据中找到异常值，解释模型所学到的输入和输出之间的关系以及调试模型）是不适当的。与边缘检测类比，本文发现一些显著性方法无需训练数据或模型就能有效实现其功能。

显著图的合理性检查

Sanity Checks for Saliency Maps

Saliency methods aim to explain the predictions of deep neural networks.
These methods lack reliability when the explanation is sensitive to factors
that do not contribute to the model prediction. We use a simple and common
pre-processing step ---adding a constant shift to the input data--- to show
that a transformation with no effect on the model can cause numerous methods to
incorrectly attribute. In order to guarantee reliability, we posit that methods
should fulfill input invariance, the requirement that a saliency method mirror
the sensitivity of the model with respect to transformations of the input. We
show, through several examples, that saliency methods that do not satisfy input
invariance result in misleading attribution.

本文探讨了深度神经网络的显著性方法在解释模型预测时的可靠性问题，提出了保证输入不变性的要求，并举例说明了不满足此要求的方法可能导致错误归因。