  Learning with noisy labels has attracted a lot of attention in recent years,
where the mainstream approaches are in pointwise manners. Meanwhile, pairwise
manners have shown great potential in supervised metric learning and
unsupervised contrastive learning. Thus, a natural question is raised: does
learning in a pairwise manner mitigate label noise? To give an affirmative
answer, in this paper, we propose a framework called Class2Simi: it transforms
data points with noisy class labels to data pairs with noisy similarity labels,
where a similarity label denotes whether a pair shares the class label or not.
Through this transformation, the reduction of the noise rate is theoretically
guaranteed, and hence it is in principle easier to handle noisy similarity
labels. Amazingly, DNNs that predict the clean class labels can be trained from
noisy data pairs if they are first pretrained from noisy data points.
Class2Simi is computationally efficient because not only this transformation is
on-the-fly in mini-batches, but also it just changes loss computation on top of
model prediction into a pairwise manner. Its effectiveness is verified by
extensive experiments.
