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The learning with noisy labels has been addressed with both discriminativeand generative models. Although discriminative models have dominated the fielddue to their simpler modeling and more efficient computational trainingprocesses, generative models offer a more effective means of disentanglingclean and noisy labels and improving the estimation of the label transitionmatrix. However, generative approaches maximize the joint likelihood of noisylabels and data using a complex formulation that only indirectly optimizes themodel of interest associating data and clean labels. Additionally, theseapproaches rely on generative models that are challenging to train and tend touse uninformative clean label priors. In this paper, we propose a newgenerative noisy-label learning approach that addresses these three issues.First, we propose a new model optimisation that directly associates data andclean labels. Second, the generative model is implicitly estimated using adiscriminative model, eliminating the inefficient training of a generativemodel. Third, we propose a new informative label prior inspired by partiallabel learning as supervision signal for noisy label learning. Extensiveexperiments on several noisy-label benchmarks demonstrate that our generativemodel provides state-of-the-art results while maintaining a similarcomputational complexity as discriminative models.

Generative Noisy-Label Learning by Implicit Dicriminative Approximation with Partial Label Prior