  Recently deep neural networks have been successfully used for various
classification tasks, especially for problems with massive perfectly labeled
training data. However, it is often costly to have large-scale credible labels
in real-world applications. One solution is to make supervised learning robust
with imperfectly labeled input. In this paper, we develop a distribution
correction approach that allows deep neural networks to avoid overfitting
imperfect training data. Specifically, we treat the noisy input as samples from
an incorrect distribution, which will be automatically corrected during our
training process. We test our approach on several classification datasets with
elaborately generated noisy labels. The results show significantly higher
prediction and recovery accuracy with our approach compared to alternative
methods.
