Prediction Consistency Regularization for Learning with Noise Labels Based on Contrastive Clustering
In the classification task, label noise has a significant impact on models’ performance, primarily manifested in the disruption of prediction consistency, thereby reducing the classification accuracy. This work introduces a novel prediction consistency regularization that mitigates the impact of lab...
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| Huvudupphov: | , , |
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| Materialtyp: | Artigo |
| Språk: | Inglês |
| Utgiven: |
MDPI AG
2024-03-01
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| Serie: | Entropy |
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| Länkar: | https://www.mdpi.com/1099-4300/26/4/308 |
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