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Authors: Bhalaji Nagarajan 1 ; Ricardo Marques 1 ; Marcos Mejia 1 and Petia Radeva 2 ; 1

Affiliations: 1 Dept. de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain ; 2 Computer Vision Center, Cerdanyola (Barcelona), Spain

Keyword(s): Noisy Labeling, Loss Correction, Class-conditional Importance Weighting, Learning with Noisy Labels.

Abstract: Large-scale accurate labels are very important to the Deep Neural Networks to train them and assure high performance. However, it is very expensive to create a clean dataset since usually it relies on human interaction. To this purpose, the labelling process is made cheap with a trade-off of having noisy labels. Learning with Noisy Labels is an active area of research being at the same time very challenging. The recent advances in Self-supervised learning and robust loss functions have helped in advancing noisy label research. In this paper, we propose a loss correction method that relies on dynamic weights computed based on the model training. We extend the existing Contrast to Divide algorithm coupled with DivideMix using a new class-conditional weighted scheme. We validate the method using the standard noise experiments and achieved encouraging results.

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Paper citation in several formats:
Nagarajan, B.; Marques, R.; Mejia, M. and Radeva, P. (2022). Class-conditional Importance Weighting for Deep Learning with Noisy Labels. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 679-686. DOI: 10.5220/0010996400003124

@conference{visapp22,
author={Bhalaji Nagarajan. and Ricardo Marques. and Marcos Mejia. and Petia Radeva.},
title={Class-conditional Importance Weighting for Deep Learning with Noisy Labels},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={679-686},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010996400003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Class-conditional Importance Weighting for Deep Learning with Noisy Labels
SN - 978-989-758-555-5
IS - 2184-4321
AU - Nagarajan, B.
AU - Marques, R.
AU - Mejia, M.
AU - Radeva, P.
PY - 2022
SP - 679
EP - 686
DO - 10.5220/0010996400003124
PB - SciTePress

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