Sharpness-Aware Minimization (SAM) has recently emerged as an effective technique for improving DNN robustness to input variations. However, its interplay with the compactness requirements of on-device DNN deployments remains less explored. Simply pruning a SAM-trained model can undermine robustness, since flatness in the continuous parameter space does not necessarily translate to robustness under the discrete structural changes induced by pruning. Conversely, applying SAM after pruning may be fundamentally constrained by architectural limitations imposed by an early, robustness-agnostic pruning pattern. To address this gap, we propose Compression-aware ShArpness Minimization (C-SAM), a framework that shifts sharpness-aware learning from parameter perturbations to mask perturbations. By explicitly perturbing pruning masks during training, C-SAM promotes a flatter loss landscape with respect to model structure, enabling the discovery of pruning patterns that simultaneously optimize model compactness and robustness to input variations. Extensive experiments on CelebA-HQ, Flowers-102, and CIFAR-10-C across ResNet-18, GoogLeNet, and MobileNet-V2 show that C-SAM consistently achieves higher certified robustness than strong baselines, with improvements of up to 42%, while maintaining task accuracy comparable to the corresponding unpruned models.

翻译：锐度感知最小化（SAM）近期已成为提升深度神经网络对输入变化鲁棒性的有效技术。然而，其与设备端深度神经网络部署所需的紧凑性要求之间的相互作用仍较少被探索。直接对SAM训练的模型进行剪枝可能会损害鲁棒性，因为连续参数空间中的平坦性未必能转化为剪枝引发的离散结构变化下的鲁棒性。反之，在剪枝后应用SAM可能从根本上受到早期、未考虑鲁棒性的剪枝模式所施加的架构限制。为填补这一空白，我们提出压缩感知锐度最小化（C-SAM），该框架将锐度感知学习从参数扰动转向掩码扰动。通过在训练期间显式扰动剪枝掩码，C-SAM促进了相对于模型结构更平坦的损失景观，从而能够发现同时优化模型紧凑性和对输入变化鲁棒性的剪枝模式。在CelebA-HQ、Flowers-102和CIFAR-10-C数据集上，针对ResNet-18、GoogLeNet和MobileNet-V2的广泛实验表明，C-SAM始终比强基线方法获得更高的认证鲁棒性，提升幅度高达42%，同时保持与对应未剪枝模型相当的任务精度。