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Deep neural networks have shown impressive performance in supervisedlearning, enabled by their ability to fit well to the provided training data.However, their performance is largely dependent on the quality of the trainingdata and often degrades in the presence of noise. We propose a principledapproach for tackling label noise with the aim of assigning importance weightsto individual instances and class labels. Our method works by formulating aclass of constrained optimization problems that yield simple closed formupdates for these importance weights. The proposed optimization problems aresolved per mini-batch which obviates the need of storing and updating theweights over the full dataset. Our optimization framework also provides atheoretical perspective on existing label smoothing heuristics for addressinglabel noise (such as label bootstrapping). We evaluate our method on severalbenchmark datasets and observe considerable performance gains in the presenceof label noise.

Constrained Instance and Class Reweighting for Robust Learning under Label Noise