  Adversarial Training (AT) with Projected Gradient Descent (PGD) is an
effective approach for improving the robustness of the deep neural networks.
However, PGD AT has been shown to suffer from two main limitations: i) high
computational cost, and ii) extreme overfitting during training that leads to
reduction in model generalization. While the effect of factors such as model
capacity and scale of training data on adversarial robustness have been
extensively studied, little attention has been paid to the effect of a very
important parameter in every network optimization on adversarial robustness:
the learning rate. In particular, we hypothesize that effective learning rate
scheduling during adversarial training can significantly reduce the overfitting
issue, to a degree where one does not even need to adversarially train a model
from scratch but can instead simply adversarially fine-tune a pre-trained
model. Motivated by this hypothesis, we propose a simple yet very effective
adversarial fine-tuning approach based on a $\textit{slow start, fast decay}$
learning rate scheduling strategy which not only significantly decreases
computational cost required, but also greatly improves the accuracy and
robustness of a deep neural network. Experimental results show that the
proposed adversarial fine-tuning approach outperforms the state-of-the-art
methods on CIFAR-10, CIFAR-100 and ImageNet datasets in both test accuracy and
the robustness, while reducing the computational cost by 8-10$\times$.
Furthermore, a very important benefit of the proposed adversarial fine-tuning
approach is that it enables the ability to improve the robustness of any
pre-trained deep neural network without needing to train the model from
scratch, which to the best of the authors' knowledge has not been previously
demonstrated in research literature.
