EBOMLC applies dynamic barrier gradient descent with one-step inner loop, mixture upper loss, and alignment-aware barrier to make meta label correction faster and more robust on noisy data, outperforming baselines on CIFAR-10/100 especially at high noise rates.
Learning from noisy labels with decou- pled meta label purifier.2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 19934–19943
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Efficient Bilevel Optimization for Meta Label Correction in Noisy Label Learning
EBOMLC applies dynamic barrier gradient descent with one-step inner loop, mixture upper loss, and alignment-aware barrier to make meta label correction faster and more robust on noisy data, outperforming baselines on CIFAR-10/100 especially at high noise rates.