Training a classifier with noisy labels typically requires the learner to specify the distribution of label noise, which is often unknown in practice. Although there have been some recent attempts to relax that requirement, we show that the Bayes decision rule is unidentified in most classification problems with noisy labels. This suggests it is generally not possible to bypass/relax the requirement. In the special cases in which the Bayes decision rule is identified, we develop a simple algorithm to learn the Bayes decision rule, that does not require knowledge of the noise distribution.

翻译：使用含噪标签训练分类器通常要求学习者指定标签噪声的分布，而这一分布在实践中往往未知。尽管近期有研究尝试放宽这一要求，但我们证明，在大多数含噪标签的分类问题中，贝叶斯决策规则是不可识别的。这表明通常不可能绕过或放宽上述要求。在贝叶斯决策规则可识别的特殊情形中，我们开发了一种无需知晓噪声分布的简单算法来学习该决策规则。