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Recently, the explanation of neural network models has garnered considerableresearch attention. In computer vision, CAM (Class Activation Map)-basedmethods and LRP (Layer-wise Relevance Propagation) method are two commonexplanation methods. However, since most CAM-based methods can only generateglobal weights, they can only generate coarse-grained explanations at a deeplayer. LRP and its variants, on the other hand, can generate fine-grainedexplanations. But the faithfulness of the explanations is too low. To addressthese challenges, in this paper, we propose FG-CAM (Fine-Grained CAM), whichextends CAM-based methods to enable generating fine-grained andhigh-faithfulness explanations. FG-CAM uses the relationship between twoadjacent layers of feature maps with resolution differences to graduallyincrease the explanation resolution, while finding the contributing pixels andfiltering out the pixels that do not contribute. Our method not only solves theshortcoming of CAM-based methods without changing their characteristics, butalso generates fine-grained explanations that have higher faithfulness than LRPand its variants. We also present FG-CAM with denoising, which is a variant ofFG-CAM and is able to generate less noisy explanations with almost no change inexplanation faithfulness. Experimental results show that the performance ofFG-CAM is almost unaffected by the explanation resolution. FG-CAM outperformsexisting CAM-based methods significantly in both shallow and intermediatelayers, and outperforms LRP and its variations significantly in the inputlayer. Our code is available at https://github.com/dongmo-qcq/FG-CAM.

Empowering CAM-based Methods with Capability to Generate Fine-Grained and High-Faithfulness Explanations