  The major driving force behind the immense success of deep learning models is
the availability of large datasets along with their clean labels.
Unfortunately, this is very difficult to obtain, which has motivated research
on the training of deep models in the presence of label noise and ways to avoid
over-fitting on the noisy labels. In this work, we build upon the seminal work
in this area, Co-teaching and propose a simple, yet efficient approach termed
mCT-S2R (modified co-teaching with self-supervision and re-labeling) for this
task. First, to deal with significant amount of noise in the labels, we propose
to use self-supervision to generate robust features without using any labels.
Next, using a parallel network architecture, an estimate of the clean labeled
portion of the data is obtained. Finally, using this data, a portion of the
estimated noisy labeled portion is re-labeled, before resuming the network
training with the augmented data. Extensive experiments on three standard
datasets show the effectiveness of the proposed framework.
