  Increasing demands for understanding the internal behavior of convolutional
neural networks (CNNs) have led to remarkable improvements in explanation
methods. Particularly, several class activation mapping (CAM) based methods,
which generate visual explanation maps by a linear combination of activation
maps from CNNs, have been proposed. However, the majority of the methods lack a
clear theoretical basis on how they assign the coefficients of the linear
combination. 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. With this approach, the explanation model can be
determined by additive feature attribution methods in an analytic manner. We
then demonstrate the adequacy of SHAP values, which is a unique solution for
the explanation model with a set of desirable properties, as the coefficients
of CAM. Since the exact SHAP values are unattainable, we introduce an efficient
approximation method, LIFT-CAM, based on DeepLIFT. Our proposed LIFT-CAM can
estimate the SHAP values of the activation maps with high speed and accuracy.
Furthermore, it greatly outperforms other previous CAM-based methods in both
qualitative and quantitative aspects.
