ICML 2020poster91 citations
Deep k-NN for Noisy Labels
Dara Bahri, Heinrich Jiang, Maya Gupta
Abstract
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.
BibTeX
@InProceedings{pmlr-v119-bahri20a,
title = {Deep k-{NN} for Noisy Labels},
author = {Bahri, Dara and Jiang, Heinrich and Gupta, Maya},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
pages = {540--550},
year = {2020},
editor = {III, Hal Daumé and Singh, Aarti},
volume = {119},
series = {Proceedings of Machine Learning Research},
month = {13--18 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v119/bahri20a/bahri20a.pdf},
url = {https://proceedings.mlr.press/v119/bahri20a.html},
abstract = {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.}
}