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Authors: Nesreen Hasan ; Farzin Ghorban ; Jörg Velten and Anton Kummert

Affiliation: Faculty of Electrical, Information and Media Engineering, University of Wuppertal, Germany

Keyword(s): Semi-Supervised Learning, Consistency Regularization, Data Augmentation.

Abstract: We propose a framework, CGT, for semi-supervised learning (SSL) that involves a unification of multiple image-based augmentation techniques. More specifically, we utilize Mixup and CutMix in addition to introducing one-sided stochastically augmented versions of those operators. Moreover, we introduce a generalization of the Mixup operator that regularizes a larger region of the input space. The objective of CGT is expressed as a linear combination of multiple constituents, each corresponding to the contribution of a different augmentation technique. CGT achieves state-of-the-art performance on the SVHN, CIFAR-10, and CIFAR-100 benchmark datasets and demonstrates that it is beneficial to heavily augment unlabeled training data.

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Paper citation in several formats:
Hasan, N.; Ghorban, F.; Velten, J. and Kummert, A. (2022). CGT: Consistency Guided Training in Semi-Supervised Learning. 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 55-64. DOI: 10.5220/0010771700003124

@conference{visapp22,
author={Nesreen Hasan. and Farzin Ghorban. and Jörg Velten. and Anton Kummert.},
title={CGT: Consistency Guided Training in Semi-Supervised Learning},
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={55-64},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010771700003124},
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 - CGT: Consistency Guided Training in Semi-Supervised Learning
SN - 978-989-758-555-5
IS - 2184-4321
AU - Hasan, N.
AU - Ghorban, F.
AU - Velten, J.
AU - Kummert, A.
PY - 2022
SP - 55
EP - 64
DO - 10.5220/0010771700003124
PB - SciTePress

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