Counting 2,784 Big Data & Machine Learning Frameworks, Toolsets, and Examples...
Suggestion? Feedback? Tweet @stkim1

Author
Last Commit
May. 16, 2018
Created
Feb. 14, 2018

Gold Loss Correction

This repository contains the code for the paper

Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise.

Requires Python 3+ and PyTorch 0.3+.

Overview

The Gold Loss Correction (GLC) is a semi-verified method for label noise robustness in deep learning classifiers. Using a small set of data with trusted labels, we estimate parameters of the label noise, which we then use to train a corrected classifier on the noisy labels. We observe large gains in performance over prior work, with a subset of results shown below. Please consult the paper for the full results and method descriptions.

Citation

If you find this useful in your research, please consider citing:

@article{hendrycks2018glc,
  title={Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise},
  author={Hendrycks, Dan and Mazeika, Mantas and Wilson, Duncan and Gimpel, Kevin},
  journal={arXiv preprint arXiv:1802.05300},
  year={2018}
}