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cs.CV2026
GAMR: Geometric-Aware Manifold Regularization with Virtual Outlier Synthesis for Learning with Noisy Labels
Ningkang Peng, Jingyang Mao, Xiaoqian Peng +4
Deep neural networks (DNNs) experience significant performance degradation when processing noisy labels, primarily due to overfitting on mislabeled data. Current mainstream approac…
cs.CV2026
Holistic Reliability Propagation: Decoupling Annotation and Prediction for Robust Noisy-Label
Jingyang Mao, Ningkang Peng, Yanhui Gu
Learning with noisy labels in multimedia classification often combines external annotations and model predictions into a single reliability weight, even though the two sources can…
cs.CV2026
HamBR: Active Decision Boundary Restoration Based on Hamiltonian Dynamics for Learning with Noisy Labels
Ningkang Peng, Jingyang Mao, Qianfeng Yu +3
In large-scale visual recognition and data mining tasks, the presence of noisy labels severely undermines the generalization capability of deep neural networks (DNNs). Prevalent sa…