Coordinated Sparse Recovery: A Robust Approach for Learning with Instance-Dependent Label Noise

The core message of this paper is to introduce a novel approach called Coordinated Sparse Recovery (CSR) that addresses the issue of non-coordinated learning between model prediction and label noise recovery in over-parameterized networks, thereby enhancing the generalization performance under instance-dependent label noise.