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Found 2 papers, 1 papers with code
SEEN: Sharpening Explanations for Graph Neural Networks using Explanations from Neighborhoods
SEEN provides a simple but effective method to enhance the explanation quality of GNN model outputs, and this method is applicable in combination with most explainability techniques.
Towards Better Explanations of Class Activation Mapping
In this paper, we revisit the intrinsic linearity of CAM with respect to the activation maps; we construct an explanation model of CNN as a linear function of binary variables that denote the existence of the corresponding activation maps.
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