---
_id: '45911'
abstract:
- lang: eng
  text: "Label noise poses an important challenge in machine learning, especially
    in\r\ndeep learning, in which large models with high expressive power dominate
    the\r\nfield. Models of that kind are prone to memorizing incorrect labels, thereby\r\nharming
    generalization performance. Many methods have been proposed to address\r\nthis
    problem, including robust loss functions and more complex label correction\r\napproaches.
    Robust loss functions are appealing due to their simplicity, but\r\ntypically
    lack flexibility, while label correction usually adds substantial\r\ncomplexity
    to the training setup. In this paper, we suggest to address the\r\nshortcomings
    of both methodologies by \"ambiguating\" the target information,\r\nadding additional,
    complementary candidate labels in case the learner is not\r\nsufficiently convinced
    of the observed training label. More precisely, we\r\nleverage the framework of
    so-called superset learning to construct set-valued\r\ntargets based on a confidence
    threshold, which deliver imprecise yet more\r\nreliable beliefs about the ground-truth,
    effectively helping the learner to\r\nsuppress the memorization effect. In an
    extensive empirical evaluation, our\r\nmethod demonstrates favorable learning
    behavior on synthetic and real-world\r\nnoise, confirming the effectiveness in
    detecting and correcting erroneous\r\ntraining labels."
author:
- first_name: Julian
  full_name: Lienen, Julian
  id: '44040'
  last_name: Lienen
- first_name: Eyke
  full_name: Hüllermeier, Eyke
  id: '48129'
  last_name: Hüllermeier
citation:
  ama: Lienen J, Hüllermeier E. Mitigating Label Noise through Data Ambiguation. <i>arXiv:230513764</i>.
    Published online 2023.
  apa: Lienen, J., &#38; Hüllermeier, E. (2023). Mitigating Label Noise through Data
    Ambiguation. In <i>arXiv:2305.13764</i>.
  bibtex: '@article{Lienen_Hüllermeier_2023, title={Mitigating Label Noise through
    Data Ambiguation}, journal={arXiv:2305.13764}, author={Lienen, Julian and Hüllermeier,
    Eyke}, year={2023} }'
  chicago: Lienen, Julian, and Eyke Hüllermeier. “Mitigating Label Noise through Data
    Ambiguation.” <i>ArXiv:2305.13764</i>, 2023.
  ieee: J. Lienen and E. Hüllermeier, “Mitigating Label Noise through Data Ambiguation,”
    <i>arXiv:2305.13764</i>. 2023.
  mla: Lienen, Julian, and Eyke Hüllermeier. “Mitigating Label Noise through Data
    Ambiguation.” <i>ArXiv:2305.13764</i>, 2023.
  short: J. Lienen, E. Hüllermeier, ArXiv:2305.13764 (2023).
date_created: 2023-07-09T11:25:48Z
date_updated: 2023-07-09T11:26:21Z
external_id:
  arxiv:
  - '2305.13764'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/2305.13764
oa: '1'
publication: arXiv:2305.13764
status: public
title: Mitigating Label Noise through Data Ambiguation
type: preprint
user_id: '44040'
year: '2023'
...