分类器鲁棒性的内在定义
Towards an Intrinsic Definition of Robustness for a Classifier
摘要：
在过去的几年中，分类器的鲁棒性已经成为一个至关重要的问题。事实上，已经证明，最先进的深度学习架构很容易被其输入的细微变化所欺骗。因此，找到一个训练好的分类器的鲁棒性的良好措施是该领域的一个关键问题。在本文中，我们指出，平均的样本在一个验证集的鲁棒性半径是一个统计上弱的措施。相反，我们建议根据样本的难度来衡量样本的重要性。我们激励提出的分数通过使用逻辑回归的理论案例研究，在那里我们表明，提出的分数是独立的选择的样本进行评估。我们还根据经验证明了所提出的分数能够在更复杂的环境中测量分类器的鲁棒性，而对样本的选择几乎没有依赖性，包括深度卷积神经网络和真实数据集。
英文摘要：
The robustness of classifiers has become a question of paramount importance in the past few years. Indeed, it has been shown that state-of-the-art deep learning architectures can easily be fooled with imperceptible changes to their inputs. Therefore, finding good measures of robustness of a trained classifier is a key issue in the field. In this paper, we point out that averaging the radius of robustness of samples in a validation set is a statistically weak measure. We propose instead to weight the importance of samples depending on their difficulty. We motivate the proposed score by a theoretical case study using logistic regression, where we show that the proposed score is independent of the choice of the samples it is evaluated upon. We also empirically demonstrate the ability of the proposed score to measure robustness of classifiers with little dependence on the choice of samples in more complex settings, including deep convolutional neural networks and real datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
