  Given the great success of Deep Neural Networks(DNNs) and the black-box
nature of it,the interpretability of these models becomes an important
issue.The majority of previous research works on the post-hoc interpretation of
a trained model.But recently, adversarial training shows that it is possible
for a model to have an interpretable input-gradient through
training.However,adversarial training lacks efficiency for interpretability.To
resolve this problem, we construct an approximation of the adversarial
perturbations and discover a connection between adversarial training and
amplitude modulation. Based on a digital analogy,we propose noise modulation as
an efficient and model-agnostic alternative to train a model that interprets
itself with input-gradients.Experiment results show that noise modulation can
effectively increase the interpretability of input-gradients model-agnosticly.
