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Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 3/2023

15.06.2023 | Theoretical Advances

Self-label correction for image classification with noisy labels

verfasst von: Yu Zhang, Fan Lin, Siya Mi, Yali Bian

Erschienen in: Pattern Analysis and Applications | Ausgabe 3/2023

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Abstract

Label noise is inevitable in image classification. Existing methods usually lack the reliability of selecting clean data samples and rely on an auxiliary model to correct clean samples, which quality has a great impact on the classification results. In this paper, we propose the Dual-model and Self-Label Correction (DSLC) method to select clean samples and correct labels without auxiliary models. First, we use a dual-model structure combining contrastive learning to select clean samples. Then, we design a novel label correction method to modify the noisy labels. Finally, we propose a joint loss to improve the generalization ability of our models. In the experiment, we demonstrate the effectiveness of DSLC on various datasets, which achieves comparable performance to state-of-the-art methods.
Vorheriger Artikel A multimodal fusion-based deep learning framework combined with keyframe extraction and spatial and channel attention for group emotion recognition from videos
Nächster Artikel Multiple kernel k-means clustering with block diagonal property

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Metadaten
Titel
Self-label correction for image classification with noisy labels
verfasst von
Yu Zhang
Fan Lin
Siya Mi
Yali Bian
Publikationsdatum
15.06.2023
Verlag
Springer London
Erschienen in
Pattern Analysis and Applications / Ausgabe 3/2023
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-023-01180-w

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