  Supervised learning on Deep Neural Networks (DNNs) is data hungry. Optimizing
performance of DNN in the presence of noisy labels has become of paramount
importance since collecting a large dataset will usually bring in noisy labels.
Inspired by the robustness of K-Nearest Neighbors (KNN) against data noise, in
this work, we propose to apply deep KNN for label cleanup. Our approach
leverages DNNs for feature extraction and KNN for ground-truth label inference.
We iteratively train the neural network and update labels to simultaneously
proceed towards higher label recovery rate and better classification
performance. Experiment results show that under the same setting, our approach
outperforms existing label correction methods and achieves better accuracy on
multiple datasets, e.g.,76.78% on Clothing1M dataset.
