Corpus ID: 212628319

No Regret Sample Selection with Noisy Labels

@article{Mitsuo2020NoRS,
  title={No Regret Sample Selection with Noisy Labels},
  author={N. Mitsuo and S. Uchida and D. Suehiro},
  journal={ArXiv},
  year={2020},
  volume={abs/2003.03179}
}

N. Mitsuo, S. Uchida, D. Suehiro

Published 2020

Computer Science, Mathematics

ArXiv

The Deep Neural Network (DNN) suffers from noisy labeled data because of the risk of overfitting. To avoid the risk, in this paper, we propose a novel sample selection framework for learning noisy samples. The core idea is to employ a "regret" minimization approach. The proposed sample selection method adaptively selects a subset of noisy labeled training samples to minimize the regret of selecting noise samples. The algorithm works efficiently and performs with theoretical support. Moreover… CONTINUE READING

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