---
res:
  bibo_abstract:
  - "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.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Julian
      foaf_name: Lienen, Julian
      foaf_surname: Lienen
      foaf_workInfoHomepage: http://www.librecat.org/personId=44040
  - foaf_Person:
      foaf_givenName: Eyke
      foaf_name: Hüllermeier, Eyke
      foaf_surname: Hüllermeier
      foaf_workInfoHomepage: http://www.librecat.org/personId=48129
  dct_date: 2023^xs_gYear
  dct_language: eng
  dct_title: Mitigating Label Noise through Data Ambiguation@
...