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Noise Modulation: Let Your Model Interpret Itself

2021-03-19 02:55:33
Haoyang Li, Xinggang Wang

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 this http URL 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.

Abstract (translated)

URL

https://arxiv.org/abs/2103.10603

PDF

https://arxiv.org/pdf/2103.10603.pdf


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