Learning with noisy labels is a common challenge in supervised learning.
Existing approaches often require practitioners to specify noise rates, i.e., a
set of parameters controlling the severity of label noises in the problem, and
the specifications are either assumed to be given or estimated using additional
steps. In this work, we introduce a new family of loss functions that we name
as peer loss functions, which enables learning from noisy labels and does not
require a priori specification of the noise rates. Peer loss functions work
within the standard empirical risk minimization (ERM) framework. 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. We pair our
results with an extensive set of experiments. 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。