  Increasing demands for understanding the internal behaviors 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
theoretical basis in how to assign their weighted linear coefficients. In this
paper, we revisit the intrinsic linearity of CAM w.r.t. the activation maps.
Focusing on the linearity, we construct an explanation model as a linear
function of binary variables which denote the existence of the corresponding
activation maps. With this approach, the explanation model can be determined by
the class of additive feature attribution methods which adopts SHAP values as a
unified measure of feature importance. We then demonstrate the efficacy of the
SHAP values as the weight coefficients for CAM. However, the exact SHAP values
are incalculable. Hence, we introduce an efficient approximation method,
referred to as LIFT-CAM. On the basis of DeepLIFT, our proposed method can
estimate the true SHAP values quickly and accurately. Furthermore, it achieves
better performances than the other previous CAM-based methods in qualitative
and quantitative aspects.
