CAM-based methods are widely-used post-hoc interpretability method that
produce a saliency map to explain the decision of an image classification
model. The saliency map highlights the important areas of the image relevant to
the prediction. In this paper, we show that most of these methods can
incorrectly attribute an important score to parts of the image that the model
cannot see. We show that this phenomenon occurs both theoretically and
experimentally. On the theory side, we analyze the behavior of GradCAM on a
simple masked CNN model at initialization. Experimentally, we train a VGG-like
model constrained to not use the lower part of the image and nevertheless
observe positive scores in the unseen part of the image. This behavior is
evaluated quantitatively on two new datasets. We believe that this is
problematic, potentially leading to mis-interpretation of the model's behavior.

CAM-based 方法是广泛使用的事后解释性方法，通过生成热点图来解释图像分类模型的决策。在本文中，我们展示了大部分这些方法可能会错误地将重要分数归因于模型无法看到的图像部分。我们从理论和实验两方面分析了该现象，并通过两个新数据集进行了定量评估。我们认为这是有问题的，可能导致对模型行为的错误解释。