A Novel Transition Matrix Method with Implicit Regularization for Diverse Label Noise Learning

We present a novel and efficient approach, leveraging a transition matrix method in conjunction with implicit regularization, to effectively address the challenge of learning from data with diverse label noise. Our method focuses on estimating a global matrix, and we provide comprehensive theoretical analysis to validate the effectiveness of our algorithm.