Deep k-NN for Noisy Labels

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 mislabe...

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Bibliographische Detailangaben

Hauptverfasser:	Bahri, Dara, Jiang, Heinrich, Gupta, Maya
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Statistics - Machine Learning
Online-Zugang:	Volltext bestellen
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