  Prior gradient-based attribution-map methods rely on handcrafted propagation
rules for the non-linear/activation layers during the backward pass, so as to
produce gradients of the input and then the attribution map. Despite the
promising results achieved, such methods are sensitive to the non-informative
high-frequency components and lack adaptability for various models and samples.
In this paper, we propose a dedicated method to generate attribution maps that
allow us to learn the propagation rules automatically, overcoming the flaws of
the handcrafted ones. Specifically, we introduce a learnable plugin module,
which enables adaptive propagation rules for each pixel, to the non-linear
layers during the backward pass for mask generating. The masked input image is
then fed into the model again to obtain new output that can be used as a
guidance when combined with the original one. The introduced learnable module
can be trained under any auto-grad framework with higher-order differential
support. As demonstrated on five datasets and six network architectures, the
proposed method yields state-of-the-art results and gives cleaner and more
visually plausible attribution maps.
