注意营养：使用随机噪声的高斯过程分类
Mind the Nuisance: Gaussian Process Classification using Privileged Noise
摘要：
特权信息设置的学习最近在机器学习社区中引起了很多关注，因为它允许将额外的知识集成到分类器的训练过程中，即使这是以测试时不可用的数据模态的形式出现。在这里，我们表明，特权信息可以自然地被视为噪声的潜在功能的高斯过程分类器（GPC）。也就是说，与标准GPC设置相比，潜在函数不仅仅是一个讨厌的东西，而是一个功能：它通过调制GPC S形似然函数的斜率而成为关于训练数据的置信度的自然度量。在公共数据集上进行的大量实验表明，所提出的使用特权噪声的GPC方法（称为GPC+）优于没有特权知识的标准GPC，也优于当前最先进的基于SVM的方法SVM+。此外，我们证明了先进的神经网络和深度学习方法可以被压缩为特权信息。
英文摘要：
The learning with privileged information setting has recently attracted a lot of attention within the machine learning community, as it allows the integration of additional knowledge into the training process of a classifier, even when this comes in the form of a data modality that is not available at test time. Here, we show that privileged information can naturally be treated as noise in the latent function of a Gaussian Process classifier (GPC). That is, in contrast to the standard GPC setting, the latent function is not just a nuisance but a feature: it becomes a natural measure of confidence about the training data by modulating the slope of the GPC sigmoid likelihood function. Extensive experiments on public datasets show that the proposed GPC method using privileged noise, called GPC+, improves over a standard GPC without privileged knowledge, and also over the current state-of-the-art SVM-based method, SVM+. Moreover, we show that advanced neural networks and deep learning methods can be compressed as privileged information.
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。
