One-Step Abductive Multi-Target Learning with Diverse Noisy Label Samples
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Abstract (arXiv)
One-step abductive multi-target learning (OSAMTL) was proposed to handle complex noisy labels. In this paper, giving definition of diverse noisy label samples (DNLS), we propose one-step abductive multi-target learning with DNLS (OSAMTL-DNLS) to expand the methodology of original OSAMTL to better handle complex noisy labels.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 15 references that are in the map. First posted on arXiv 8 Dec 2021.
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11 of 15 references.
References · 11
- 2020DivideMix: Learning with Noisy Labels as Semi-supervised Learningcited by 691
- 2016Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approachcited by 663
- 2019Symmetric Cross Entropy for Robust Learning with Noisy Labelscited by 499
- 2019Unsupervised Label Noise Modeling and Loss Correctioncited by 327
- 2014Training Deep Neural Networks on Noisy Labels with Bootstrappingcited by 322
- 2020Normalized Loss Functions for Deep Learning with Noisy Labelscited by 257
- 2018Dimensionality-Driven Learning with Noisy Labelscited by 173
- 2019Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Ratescited by 142
- 2020Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learningcited by 142
- 2019Devil is in the Edges: Learning Semantic Boundaries from Noisy Annotationscited by 32
- 2021Logical Assessment Formula and Its Principles for Evaluations with Inaccurate Ground-Truth Labelscited by 3
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