  Deep learning has achieved excellent performance in various computer vision
tasks, but requires a lot of training examples with clean labels. It is easy to
collect a dataset with noisy labels, but such noise makes networks overfit
seriously and accuracies drop dramatically. To address this problem, we propose
an end-to-end framework called PENCIL, which can update both network parameters
and label estimations as label distributions. PENCIL is independent of the
backbone network structure and does not need an auxiliary clean dataset or
prior information about noise, thus it is more general and robust than existing
methods and is easy to apply. PENCIL outperforms previous state-of-the-art
methods by large margins on both synthetic and real-world datasets with
different noise types and noise rates. Experiments show that PENCIL is robust
on clean datasets, too.
