  Integrated gradients are widely employed to evaluate the contribution of
input features in classification models because it satisfies the axioms for
attribution of prediction. This method, however, requires an appropriate
baseline for reliable determination of the contributions. We propose a
compensated integrated gradients method that does not require a baseline. In
fact, the method compensates the attributions calculated by integrated
gradients at an arbitrary baseline using Shapley sampling. We prove that the
method retrieves reliable attributions if the processes of input features in a
classifier are mutually independent, and they are identical like shared weights
in convolutional neural networks. Using three electroencephalogram datasets, we
experimentally demonstrate that the attributions of the proposed method are
more reliable than those of the original integrated gradients, and its
computational complexity is much lower than that of Shapley sampling.
