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Deep k-NN for Noisy Labels
Dara Bahri · Heinrich Jiang · Maya Gupta

Tue Jul 14 09:00 AM -- 09:45 AM & Wed Jul 15 08:00 PM -- 08:45 PM (PDT) @ None #None
Modern machine learning models are often trained on examples with noisy labels that hurt performance and are hard to identify. In this paper, we provide an empirical study showing that a simple $k$-nearest neighbor-based filtering approach on the logit layer of a preliminary model can remove mislabeled training data and produce more accurate models than many recently proposed methods. We also provide new statistical guarantees into its efficacy.

Author Information

Dara Bahri (Google Research)
Heinrich Jiang (Google Research)
Maya Gupta (Google)

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