Adversarial training improves the robustness of neural networks against adversarial attacks, albeit at the expense of the trade-off between standard and robust generalization. To unveil the underlying factors driving this phenomenon, we examine the layer-wise learning capabilities of neural networks during the transition from a standard to an adversarial setting. Our empirical findings demonstrate that selectively updating specific layers while preserving others can substantially enhance the network's learning capacity. We therefore propose CURE, a novel training framework that leverages a gradient prominence criterion to perform selective conservation, updating, and revision of weights. Importantly, CURE is designed to be dataset- and architecture-agnostic, ensuring its applicability across various scenarios. It effectively tackles both memorization and overfitting issues, thus enhancing the trade-off between robustness and generalization and additionally, this training approach also aids in mitigating "robust overfitting". Furthermore, our study provides valuable insights into the mechanisms of selective adversarial training and offers a promising avenue for future research.

对神经网络在标准和对抗环境下学习能力的分层能力进行研究，提出了CURE训练框架，通过选择性保留、更新和修订权重来增强网络的学习能力，从而改善鲁棒性和泛化之间的权衡，并且有助于减轻过拟合问题和“鲁棒过拟合”，为未来研究提供了有价值的见解。

保守-更新-修订: 通过对抗训练来解决泛化与稳健性的抉择