  Real-world data inevitably contains noisy labels, which induce the poor
generalization of deep neural networks. It is known that the network typically
begins to rapidly memorize false-labeled samples after a certain point of
training. Thus, to counter the label noise challenge, we propose a novel
self-transitional learning method called MORPH, which automatically switches
its learning phase at the transition point from seeding to evolution. In the
seeding phase, the network is updated using all the samples to collect a seed
of clean samples. Then, in the evolution phase, the network is updated using
only the set of arguably clean samples, which precisely keeps expanding by the
updated network. Thus, MORPH effectively avoids the overfitting to
false-labeled samples throughout the entire training period. Extensive
experiments using five real-world or synthetic benchmark datasets demonstrate
substantial improvements over state-of-the-art methods in terms of robustness
and efficiency.
