  The learning with noisy labels has been addressed with both discriminative
and generative models. Although discriminative models have dominated the field
due to their simpler modeling and more efficient computational training
processes, generative models offer a more effective means of disentangling
clean and noisy labels and improving the estimation of the label transition
matrix. However, generative approaches maximize the joint likelihood of noisy
labels and data using a complex formulation that only indirectly optimizes the
model of interest associating data and clean labels. Additionally, these
approaches rely on generative models that are challenging to train and tend to
use uninformative clean label priors. In this paper, we propose a new
generative noisy-label learning approach that addresses these three issues.
First, we propose a new model optimisation that directly associates data and
clean labels. Second, the generative model is implicitly estimated using a
discriminative model, eliminating the inefficient training of a generative
model. Third, we propose a new informative label prior inspired by partial
label learning as supervision signal for noisy label learning. Extensive
experiments on several noisy-label benchmarks demonstrate that our generative
model provides state-of-the-art results while maintaining a similar
computational complexity as discriminative models.

Computer Vision and Pattern Recognition;Machine Learning