Abstract:
With the rapid development of deep neural networks, model compression technology has become indispensable in the process of making deep learning models reliably deployed on embedded systems with limited resources. At the same time, exploring the adversarial robustness of neural networks has recently gained more and more attention, because recent works have shown that these models are susceptible to adversarial attacks. Model compression and robustness play an important role in the deep learning model from landing to practical application scenarios. However, in the existing literature, the two have been mostly studied independently, so this paper aims to combining model compression and robustness to make the model compact and robust concurrently. In the framework of adversarial training, we have studied some of the properties of the relationship between model compression and model robustness. And it is proved by experiments that the model compression and the anti-bracket robustness can be obtained simultaneously.
