  The drastic increase of data quantity often brings the severe decrease of
data quality, such as incorrect label annotations, which poses a great
challenge for robustly training Deep Neural Networks (DNNs). Existing learning
\mbox{methods} with label noise either employ ad-hoc heuristics or restrict to
specific noise assumptions. However, more general situations, such as
instance-dependent label noise, have not been fully explored, as scarce studies
focus on their label corruption process. By categorizing instances into
confusing and unconfusing instances, this paper proposes a simple yet universal
probabilistic model, which explicitly relates noisy labels to their instances.
The resultant model can be realized by DNNs, where the training procedure is
accomplished by employing an alternating optimization algorithm. Experiments on
datasets with both synthetic and real-world label noise verify that the
proposed method yields significant improvements on robustness over
state-of-the-art counterparts.
