Leveraging privileged information (PI), or features available during training but not at test time, has recently been shown to be an effective method for addressing label noise. However, the reasons for its effectiveness are not well understood. In this study, we investigate the role played by different properties of the PI in explaining away label noise. Through experiments on multiple datasets with real PI (CIFAR-N/H) and a new large-scale benchmark ImageNet-PI, we find that PI is most helpful when it allows networks to easily distinguish clean from noisy data, while enabling a learning shortcut to memorize the noisy examples. Interestingly, when PI becomes too predictive of the target label, PI methods often perform worse than their no-PI baselines. Based on these findings, we propose several enhancements to the state-of-the-art PI methods and demonstrate the potential of PI as a means of tackling label noise. Finally, we show how we can easily combine the resulting PI approaches with existing no-PI techniques designed to deal with label noise.

翻译：利用特权信息（即在训练阶段可用但测试阶段不可用的特征）最近被证明是处理标签噪声的有效方法。然而，其有效性背后的原因尚不明确。本研究通过探究特权信息的不同属性在解释标签噪声中的作用，基于多个包含真实特权信息的数据集（CIFAR-N/H）及新的大规模基准ImageNet-PI的实验发现：当特权信息能使网络轻松区分清洁数据与噪声数据，同时为记忆噪声样本提供学习捷径时，其效果最为显著。值得注意的是，当特权信息对目标标签的预测能力过强时，基于特权信息的方法往往表现差于无特权信息的基线方法。基于这些发现，我们提出了对现有最优特权信息方法的若干改进，并展示了特权信息作为处理标签噪声手段的潜力。最后，我们论证了如何将由此产生的特权信息方法与现有的无特权信息噪声处理技术进行便捷结合。