@inproceedings{
zhu2024label,
title={Label Noise: Ignorance Is Bliss},
author={Yilun Zhu and Jianxin Zhang and Aditya Gangrade and Clayton Scott},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=fTKcqr4xuX}
}
We establish a new theoretical framework for learning under multi-class, instance-dependent label noise. 
    This framework casts learning with label
    noise as a form of domain adaptation, in particular, domain adaptation
    under posterior drift. 
    We introduce the concept of \emph{relative signal strength} (RSS), a pointwise measure that quantifies the transferability from noisy to clean posterior. 
    Using RSS, we establish nearly matching upper and lower bounds on the excess risk. 
    Our theoretical findings support 
    the simple
    \emph{Noise Ignorant Empirical Risk Minimization (NI-ERM)} principle,
    which minimizes empirical risk while ignoring label noise.
    Finally, we translate this theoretical insight into practice: by
    using NI-ERM to fit a linear classifier on top of a self-supervised
    feature extractor, we achieve state-of-the-art performance on the
    CIFAR-N data challenge.
Label Noise: Ignorance Is Bliss
Yilun Zhu Jianxin Zhang Aditya Gangrade Clayton Scott