Learning with noisy labels is a common problem in supervised learning. Existing approaches require practitioners to specify \emph{noise rates}, i.e., a set of parameters controlling the severity of label noises in the problem. In this work, we introduce a technique to learn from noisy labels that does not require a priori specification of the noise rates. In particular, we introduce a new family of loss functions that we name as \emph{peer loss} functions. Our approach then uses a standard empirical risk minimization (ERM) framework with peer loss functions. Peer loss functions associate each training sample with a certain form of "peer" samples, which evaluate a classifier' predictions jointly. We show that, under mild conditions, performing ERM with peer loss functions on the noisy dataset leads to the optimal or a near optimal classifier as if performing ERM over the clean training data, which we do not have access to. To our best knowledge, this is the first result on "learning with noisy labels without knowing noise rates" with theoretical guarantees. We pair our results with an extensive set of experiments, where we compare with state-of-the-art techniques of learning with noisy labels. Our results show that peer loss functions based method consistently outperforms the baseline benchmarks. Peer loss provides a way to simplify model development when facing potentially noisy training labels, and can be promoted as a robust candidate loss function in such situations.

本文提出一种新的损失函数家族--对等损失函数，用于解决监督学习中的噪声标签问题，不需要事先指定噪声率，可以获得与训练数据相同的最优分类器，并进行了大量实验验证，是解决标签可能含有噪声的训练中的一种稳健方法。

同伴损失函数：在不知道噪声比率的情况下从有噪标签中学习