论文标题
通过基于噪声的增强来实现有效的以数据为中心的稳健机器学习
Towards Efficient Data-Centric Robust Machine Learning with Noise-based Augmentation
论文作者
论文摘要
以数据为中心的机器学习旨在找到有效的方法来构建适当的数据集，以改善AI模型的性能。在本文中，我们主要集中于设计一个有效的以数据为中心的方案，以提高模型在黑盒测试设置中无法预见的恶意输入方面的鲁棒性。具体而言，我们引入了一种基于液体的数据增强方法，该方法由高斯噪声，盐和辣椒噪声和PGD对抗扰动组成。所提出的方法是基于轻量级算法构建的，并基于全面的评估而被证明是高效的，显示了计算成本和稳健性增强的效率。此外，我们分享了有关从我们的实验中获得的以数据为中心的鲁棒机器学习的见解。
The data-centric machine learning aims to find effective ways to build appropriate datasets which can improve the performance of AI models. In this paper, we mainly focus on designing an efficient data-centric scheme to improve robustness for models towards unforeseen malicious inputs in the black-box test settings. Specifically, we introduce a noised-based data augmentation method which is composed of Gaussian Noise, Salt-and-Pepper noise, and the PGD adversarial perturbations. The proposed method is built on lightweight algorithms and proved highly effective based on comprehensive evaluations, showing good efficiency on computation cost and robustness enhancement. In addition, we share our insights about the data-centric robust machine learning gained from our experiments.
