  Deep neural network can easily overfit to even noisy labels due to its high
capacity, which degrades the generalization performance of a model. To overcome
this issue, we propose a new approach for learning from noisy labels (LNL) via
post-training, which can significantly improve the generalization performance
of any pre-trained model on noisy label data. To this end, we rather exploit
the overfitting property of a trained model to identify mislabeled samples.
Specifically, our post-training approach gradually removes samples with high
influence on the decision boundary and refines the decision boundary to improve
generalization performance. Our post-training approach creates great synergies
when combined with the existing LNL methods. Experimental results on various
real-world and synthetic benchmark datasets demonstrate the validity of our
approach in diverse realistic scenarios.
