  Recent studies on learning with noisy labels have shown remarkable
performance by exploiting a small clean dataset. In particular, model agnostic
meta-learning-based label correction methods further improve performance by
correcting noisy labels on the fly. However, there is no safeguard on the label
miscorrection, resulting in unavoidable performance degradation. Moreover,
every training step requires at least three back-propagations, significantly
slowing down the training speed. To mitigate these issues, we propose a robust
and efficient method that learns a label transition matrix on the fly.
Employing the transition matrix makes the classifier skeptical about all the
corrected samples, which alleviates the miscorrection issue. We also introduce
a two-head architecture to efficiently estimate the label transition matrix
every iteration within a single back-propagation, so that the estimated matrix
closely follows the shifting noise distribution induced by label correction.
Extensive experiments demonstrate that our approach shows the best performance
in training efficiency while having comparable or better accuracy than existing
methods.
