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CONCLUSION In this paper, we address the challenge of learning from noisy labels by leveraging the semantic reliability of synthetic images

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cs.CV 1

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2025 1

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Noisy Label Refinement with Semantically Reliable Synthetic Images

cs.CV · 2025-09-04 · conditional · novelty 6.0

Synthetic images generated per class serve as stable feature anchors; combining feature similarity to these anchors with classifier confidence corrects noisy labels and improves classification accuracy, especially under semantic noise.

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  • Noisy Label Refinement with Semantically Reliable Synthetic Images cs.CV · 2025-09-04 · conditional · none · ref 5

    Synthetic images generated per class serve as stable feature anchors; combining feature similarity to these anchors with classifier confidence corrects noisy labels and improves classification accuracy, especially under semantic noise.