Deep neural networks (DNNs) excel on clean images but struggle with corrupted
ones. Incorporating specific corruptions into the data augmentation pipeline
can improve robustness to those corruptions but may harm performance on clean
images and other types of distortion. In this paper, we introduce an
alternative approach that improves the robustness of DNNs to a wide range of
corruptions without compromising accuracy on clean images. We first demonstrate
that input perturbations can be mimicked by multiplicative perturbations in the
weight space. Leveraging this, we propose Data Augmentation via Multiplicative
Perturbation (DAMP), a training method that optimizes DNNs under random
multiplicative weight perturbations. We also examine the recently proposed
Adaptive Sharpness-Aware Minimization (ASAM) and show that it optimizes DNNs
under adversarial multiplicative weight perturbations. Experiments on image
classification datasets (CIFAR-10/100, TinyImageNet and ImageNet) and neural
network architectures (ResNet50, ViT-S/16) show that DAMP enhances model
generalization performance in the presence of corruptions across different
settings. Notably, DAMP is able to train a ViT-S/16 on ImageNet from scratch,
reaching the top-1 error of 23.7% which is comparable to ResNet50 without
extensive data augmentations.

深度神经网络在干净图像上表现出色，但在损坏图像上表现不佳。本文提出了一种改善深度神经网络对各种损坏的鲁棒性且不损害干净图像准确性的一种方法，该方法通过引入输入扰动，模拟在权重空间中的乘法扰动，并通过数据增强训练深度神经网络。实验证明该方法能显著提高模型的泛化性能，并在不同环境中证明了其有效性。