Noise Modulation: Let Your Model Interpret Itself

TL;DR

This paper introduces noise modulation, a novel, efficient, and model-agnostic method inspired by amplitude modulation to enhance the interpretability of deep neural networks through input-gradient analysis.

Contribution

It proposes noise modulation as a new approach that approximates adversarial perturbations, improving interpretability without the inefficiencies of adversarial training.

Findings

Noise modulation effectively increases interpretability of input-gradients.

It is model-agnostic and more efficient than adversarial training.

Experimental results validate the method's effectiveness.

Abstract

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.