HamBR uses Spherical HMC to probe ambiguous regions and synthesize virtual outliers with energy-based repulsion to restore decision boundaries degraded by noisy labels, achieving SOTA on CIFAR and real-world benchmarks.
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HRP decouples annotation reliability (alpha) and pseudo-label reliability (beta) via bilevel meta-learning and routes them to distinct objectives in reliability-aware Mixup and contrastive learning for improved noisy-label robustness.
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HamBR: Active Decision Boundary Restoration Based on Hamiltonian Dynamics for Learning with Noisy Labels
HamBR uses Spherical HMC to probe ambiguous regions and synthesize virtual outliers with energy-based repulsion to restore decision boundaries degraded by noisy labels, achieving SOTA on CIFAR and real-world benchmarks.
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Holistic Reliability Propagation: Decoupling Annotation and Prediction for Robust Noisy-Label
HRP decouples annotation reliability (alpha) and pseudo-label reliability (beta) via bilevel meta-learning and routes them to distinct objectives in reliability-aware Mixup and contrastive learning for improved noisy-label robustness.