Confident Learning: Estimating Uncertainty in Dataset Labels

31 Oct 2019Curtis G. NorthcuttLu JiangIsaac L. Chuang

Learning exists in the context of data, yet notions of \emph{confidence} typically focus on model predictions, not label quality. Confident learning (CL) has emerged as an approach for characterizing, identifying, and learning with noisy labels in datasets, based on the principles of pruning noisy data, counting to estimate noise, and ranking examples to train with confidence... (read more)

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