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.
Experimental setup Datasets and noise types
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Noisy Label Refinement with Semantically Reliable Synthetic Images
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.