  In learning tasks with label noise, improving model robustness against
overfitting is a pivotal challenge because the model eventually memorizes
labels, including the noisy ones. Identifying the samples with noisy labels and
preventing the model from learning them is a promising approach to address this
challenge. When training with noisy labels, the per-class confidence scores of
the model, represented by the class probabilities, can be reliable criteria for
assessing whether the input label is the true label or the corrupted one. In
this work, we exploit this observation and propose a novel discriminator metric
called confidence error and a sieving strategy called CONFES to differentiate
between the clean and noisy samples effectively. We provide theoretical
guarantees on the probability of error for our proposed metric. Then, we
experimentally illustrate the superior performance of our proposed approach
compared to recent studies on various settings, such as synthetic and
real-world label noise. Moreover, we show CONFES can be combined with other
state-of-the-art approaches, such as Co-teaching and DivideMix to further
improve model performance.
