Concise Transition Matrix Method for Robust Learning with Diverse Label Noise

We introduce a succinct transition matrix approach combined with implicit regularization to effectively address the challenge of learning from data containing varying levels of label noise. Furthermore, we provide a theoretical analysis to validate the efficacy of this algorithm.