103 citations · 179 across the 8 of their papers we have counts for
3 papers · 1 filter
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
Curtis G. Northcutt, Anish Athalye, Jonas Mueller
We identify label errors in the test sets of 10 of the most commonly-used computer vision, natural language, and audio datasets, and subsequently study the potential for these labe…
Confident Learning: Estimating Uncertainty in Dataset Labels
Curtis G. Northcutt, Lu Jiang, Isaac L. Chuang
Learning exists in the context of data, yet notions of confidence typically focus on model predictions, not label quality. Confident learning (CL) is an alternative approach which…
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels
Curtis G. Northcutt, Tailin Wu, Isaac L. Chuang
Noisy PN learning is the problem of binary classification when training examples may be mislabeled (flipped) uniformly with noise rate rho1 for positive examples and rho0 for negat…