论文标题
定义和优化鲁棒性的一般框架
A general framework for defining and optimizing robustness
论文作者
论文摘要
神经网络的鲁棒性最近引起了极大的兴趣。该领域的许多调查缺乏鲁棒性概念的确切共同基础。因此，在本文中，我们提出了一个严格而灵活的框架，用于定义分类器的不同类型的鲁棒性属性。我们的鲁棒性概念是基于假设，即分类器的鲁棒性应被视为独立于准确性的属性，并且应以纯粹的数学术语定义，而不依赖其测量算法程序。我们开发了一个非常通用的鲁棒性框架，该框架适用于任何类型的分类模型，并涵盖了相关的鲁棒性概念，以进行调查，从针对对抗性攻击的安全性到模型的转让性到新领域。对于两个原型，独特的鲁棒性目标，我们然后提出了基于神经网络共同训练策略的新学习方法，以获取针对这些各自目标优化的图像分类器。
Robustness of neural networks has recently attracted a great amount of interest. The many investigations in this area lack a precise common foundation of robustness concepts. Therefore, in this paper, we propose a rigorous and flexible framework for defining different types of robustness properties for classifiers. Our robustness concept is based on postulates that robustness of a classifier should be considered as a property that is independent of accuracy, and that it should be defined in purely mathematical terms without reliance on algorithmic procedures for its measurement. We develop a very general robustness framework that is applicable to any type of classification model, and that encompasses relevant robustness concepts for investigations ranging from safety against adversarial attacks to transferability of models to new domains. For two prototypical, distinct robustness objectives we then propose new learning approaches based on neural network co-training strategies for obtaining image classifiers optimized for these respective objectives.
