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Many weakly supervised classification methods employ a noise transitionmatrix to capture the class-conditional label corruption. To estimate thetransition matrix from noisy data, existing methods often need to estimate thenoisy class-posterior, which could be unreliable due to the overconfidence ofneural networks. In this work, we propose a theoretically grounded method thatcan estimate the noise transition matrix and learn a classifier simultaneously,without relying on the error-prone noisy class-posterior estimation.Concretely, inspired by the characteristics of the stochastic label corruptionprocess, we propose total variation regularization, which encourages thepredicted probabilities to be more distinguishable from each other. Under mildassumptions, the proposed method yields a consistent estimator of thetransition matrix. We show the effectiveness of the proposed method throughexperiments on benchmark and real-world datasets.

Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization