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This paper addresses a specific challenge in computer vision by applying game-theoretic principles to filter noisy synthetic data during license plate recognition training. By modeling noise vectors as competing agents, the proposed GERIS framework effectively prunes low-quality augmented instances that would otherwise degrade model robustness. The approach offers a theoretically grounded alternative to black-box selection methods, directly advancing the reliability of machine learning pipelines in pattern recognition tasks.
GERIS: A Game-Theoretic Framework for Filtering Instance-Dependent Label Noise in License Plate Data Augmentation
arXiv:2609.31731v1 Announce Type: cross Abstract: In this paper, we propose GERIS, a game-theoretic framework for instance selection in the data augmentation phase of license plate recognition systems. During augmentation, synthetic license plate images are generated and transformed using stochastic noise to simulate real-world conditions. However, certain noise configurations lead to highly distorted, unreadable images that degrade model performance by introducing instance-dependent label noise. GERIS formulates a non-cooperative game in which each noise vector competes for inclusion in the training set based on its similarity to labeled data and its contribution to model reliability. By identifying and pruning low-quality instances, GERIS improves the overall quality of the augmented dataset. Unlike traditional black-box learning methods, GERIS offers a transparent, theoretically grounded mechanism for data filtering. Experimental results demonstrate that GERIS outperforms existing instance selection methods in terms of classification accuracy and robustness.
Abstract:In this paper, we propose GERIS, a game-theoretic framework for instance selection in the data augmentation phase of license plate recognition systems. During augmentation, synthetic license plate images are generated and transformed using stochastic noise to simulate real-world conditions. However, certain noise configurations lead to highly distorted, unreadable images that degrade model performance by introducing instance-dependent label noise. GERIS formulates a non-cooperative game in which each noise vector competes for inclusion in the training set based on its similarity to labeled data and its contribution to model reliability. By identifying and pruning low-quality instances, GERIS improves the overall quality of the augmented dataset. Unlike traditional black-box learning methods, GERIS offers a transparent, theoretically grounded mechanism for data filtering. Experimental results demonstrate that GERIS outperforms existing instance selection methods in terms of classification accuracy and robustness.
23 pages, 4 figures. To appear in AUT Journal of Mathematics and Computing (AUT J Math Comput)
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2609.31731 [cs.CV]
(or arXiv:2609.31731v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.31731 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.22060/ajmc.2025.23680.1288 Focus to learn more DOI(s) linking to related resources
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