Meta-Cognitive Resonance Label Correction for Instance-Dependent Noise

Abstract

Training with instance-dependent label noise poses a critical challenge in deep learning, severely impeding model generalization. Existing methods often struggle to break the vicious cycle where corrupted labels degrade feature representations, which in turn impairs noise identification. To overcome this, we propose the Meta-Cognitive Resonance Label Correction (MecReC) framework. For feature representation, MecReC structurally decouples representation learning from label correction, leveraging a pre-trained model as a frozen anchor to ensure feature robustness. For label correction, we introduce a novel meta-cognitive resonance mechanism that aggregates multiple complementary signals to comprehensively assess label reliability. These collaborative signals are fed to a lightweight meta-weight network, which automatically learns whether and how to correct each label. Furthermore, our personalized confidence thresholding strategy guarantees a tighter error bound for label purification. Extensive experiments demonstrate that MecReC consistently outperforms SOTA methods, exhibiting superior robustness particularly under extreme noise conditions. Code and additional details are available at https://github.com/JieSu272/MecReC.