Recent years have witnessed an explosion in the development of novel
prediction-based attribution methods, which have slowly been supplanting older
gradient-based methods to explain the decisions of deep neural networks.
However, it is still not clear why prediction-based methods outperform
gradient-based ones. Here, we start with an empirical observation: these two
approaches yield attribution maps with very different power spectra, with
gradient-based methods revealing more high-frequency content than
prediction-based methods. This observation raises multiple questions: What is
the source of this high-frequency information, and does it truly reflect
decisions made by the system? Lastly, why would the absence of high-frequency
information in prediction-based methods yield better explainability scores
along multiple metrics? We analyze the gradient of three representative visual
classification models and observe that it contains noisy information emanating
from high-frequencies. Furthermore, our analysis reveals that the operations
used in Convolutional Neural Networks (CNNs) for downsampling appear to be a
significant source of this high-frequency content -- suggesting aliasing as a
possible underlying basis. We then apply an optimal low-pass filter for
attribution maps and demonstrate that it improves gradient-based attribution
methods. We show that (i) removing high-frequency noise yields significant
improvements in the explainability scores obtained with gradient-based methods
across multiple models -- leading to (ii) a novel ranking of state-of-the-art
methods with gradient-based methods at the top. We believe that our results
will spur renewed interest in simpler and computationally more efficient
gradient-based methods for explainability.

预测基于的归因方法相对于梯度基于的方法表现更好的原因是因为前者的高频信息更少，通过优化低通滤波器可以改善梯度基于的归因方法的表现。