In learning with noisy labels, the sample selection approach is very popular, which regards small-loss data as correctly labeled during training. However, losses are generated on-the-fly based on the model being trained with noisy labels, and thus large-loss data are likely but not certainly to be incorrect. There are actually two possibilities of a large-loss data point: (a) it is mislabeled, and then its loss decreases slower than other data, since deep neural networks "learn patterns first"; (b) it belongs to an underrepresented group of data and has not been selected yet. In this paper, we incorporate the uncertainty of losses by adopting interval estimation instead of point estimation of losses, where lower bounds of the confidence intervals of losses derived from distribution-free concentration inequalities, but not losses themselves, are used for sample selection. In this way, we also give large-loss but less selected data a try; then, we can better distinguish between the cases (a) and (b) by seeing if the losses effectively decrease with the uncertainty after the try. As a result, we can better explore underrepresented data that are correctly labeled but seem to be mislabeled at first glance. Experiments demonstrate that the proposed method is superior to baselines and robust to a broad range of label noise types.

翻译：在学习噪音标签时,抽样选择方法非常受欢迎,认为小额损失数据在培训期间贴上正确的标签。然而,损失是根据使用噪音标签培训的模式在现场产生的,因此,大损失数据可能但肯定不会不正确。实际上,大损失数据点有两种可能性:(a) 错误标签,然后其损失比其他数据慢,因为深层神经网络“最原始模式”;(b) 它属于一个代表性不足的数据组,尚未选定。在本文中,我们通过采用间隔估计而不是点估计损失,将损失的不确定性纳入其中,在抽样选择中使用无分布性浓度不平等造成损失但非损失本身的较低信任度范围。这样,我们也给大损失但较少选定数据一个尝试;(a) 和(b) 更好地区分案例,看损失是否在尝试后随着不确定性而有效减少。作为结果,我们可以更好地探索代表性数据的不确定性的不确定性,即采用间隔估计,而不是对损失的点估计,即使用无分布性浓度不平等(而不是损失本身本身损失)产生的损失的较低信任度范围,用于抽样选择。在这种方式上,我们也给大亏损,但是给大的损失也给大损失的标定了一个错误的标签。