  Learning with noisy labels is a practically challenging problem in weakly
supervised learning. In the existing literature, open-set noises are always
considered to be poisonous for generalization, similar to closed-set noises. In
this paper, we empirically show that open-set noisy labels can be non-toxic and
even benefit the robustness against inherent noisy labels. Inspired by the
observations, we propose a simple yet effective regularization by introducing
Open-set samples with Dynamic Noisy Labels (ODNL) into training. With ODNL, the
extra capacity of the neural network can be largely consumed in a way that does
not interfere with learning patterns from clean data. Through the lens of SGD
noise, we show that the noises induced by our method are random-direction,
conflict-free and biased, which may help the model converge to a flat minimum
with superior stability and enforce the model to produce conservative
predictions on Out-of-Distribution instances. Extensive experimental results on
benchmark datasets with various types of noisy labels demonstrate that the
proposed method not only enhances the performance of many existing robust
algorithms but also achieves significant improvement on Out-of-Distribution
detection tasks even in the label noise setting.
