论文标题
软标签会影响深神经网络的分布外检测
Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks
论文作者
论文摘要
软标记成为深度神经网络的概括和模型压缩的常见输出正则化。但是，没有探索软标签对机器学习安全的重要主题（OOD）检测的影响。在这项研究中，我们表明软标记可以确定OOD检测性能。具体而言，如何通过软标记正规化错误类的输出可能会恶化或改善OOD检测。基于经验结果，我们假定了OOD-Robust DNNS的未来工作：通过软标记的适当输出正规化可以构建OOD-Robust DNN，而无需对OOD样品进行其他培训或修改模型，同时提高了分类精度。
Soft labeling becomes a common output regularization for generalization and model compression of deep neural networks. However, the effect of soft labeling on out-of-distribution (OOD) detection, which is an important topic of machine learning safety, is not explored. In this study, we show that soft labeling can determine OOD detection performance. Specifically, how to regularize outputs of incorrect classes by soft labeling can deteriorate or improve OOD detection. Based on the empirical results, we postulate a future work for OOD-robust DNNs: a proper output regularization by soft labeling can construct OOD-robust DNNs without additional training of OOD samples or modifying the models, while improving classification accuracy.
