论文标题
ODG-Q：通过在线域概括的稳健量化
ODG-Q: Robust Quantization via Online Domain Generalization
论文作者
论文摘要
将神经网络量化为低位宽对于在资源有限的边缘硬件上的模型部署很重要。尽管量化的网络具有较小的模型大小和内存足迹，但对对抗性攻击却是脆弱的。但是，很少有方法研究量化网络的鲁棒性和训练效率。为此，我们通过将强大的量化为在线域泛化问题（称为ODG-Q）提出了一种新方法，该方法称为ODG-Q，该量子在培训期间以低成本生成了不同的对抗数据。 ODG-Q始终在各种对抗性攻击方面始终优于现有作品。例如，在CIFAR-10数据集上，ODG-Q在五次常见的白色盒子攻击下的平均改善为49.2％，在五次常见的黑色盒子攻击下平均改善21.7％，其培训成本与自然培训相似（即没有对手）。据我们所知，这项工作是在Imagenet上训练量化和二进制神经网络的第一部作品，在不同的攻击下始终如一地改善鲁棒性。我们还提供了ODG-Q的理论见解，该见解说明了受攻击数据的模型风险的约束。
Quantizing neural networks to low-bitwidth is important for model deployment on resource-limited edge hardware. Although a quantized network has a smaller model size and memory footprint, it is fragile to adversarial attacks. However, few methods study the robustness and training efficiency of quantized networks. To this end, we propose a new method by recasting robust quantization as an online domain generalization problem, termed ODG-Q, which generates diverse adversarial data at a low cost during training. ODG-Q consistently outperforms existing works against various adversarial attacks. For example, on CIFAR-10 dataset, ODG-Q achieves 49.2% average improvements under five common white-box attacks and 21.7% average improvements under five common black-box attacks, with a training cost similar to that of natural training (viz. without adversaries). To our best knowledge, this work is the first work that trains both quantized and binary neural networks on ImageNet that consistently improve robustness under different attacks. We also provide a theoretical insight of ODG-Q that accounts for the bound of model risk on attacked data.
