  Learning with curriculum has shown great effectiveness in tasks where the
data contains noisy (corrupted) labels, since the curriculum can be used to
re-weight or filter out noisy samples via proper design. However, obtaining
curriculum from a learner itself without additional supervision or feedback
deteriorates the effectiveness due to sample selection bias. Therefore, methods
that involve two or more networks have been recently proposed to mitigate such
bias. Nevertheless, these studies utilize the collaboration between networks in
a way that either emphasizes the disagreement or focuses on the agreement while
ignores the other. In this paper, we study the underlying mechanism of how
disagreement and agreement between networks can help reduce the noise in
gradients and develop a novel framework called Robust Collaborative Learning
(RCL) that leverages both disagreement and agreement among networks. We
demonstrate the effectiveness of RCL on both synthetic benchmark image data and
real-world large-scale bioinformatics data.
