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Collecting large training datasets, annotated with high-quality labels, iscostly and time-consuming. This paper proposes a novel framework for trainingdeep convolutional neural networks from noisy labeled datasets that can beobtained cheaply. The problem is formulated using an undirected graphical modelthat represents the relationship between noisy and clean labels, trained in asemi-supervised setting. In our formulation, the inference over latent cleanlabels is tractable and is regularized during training using auxiliary sourcesof information. The proposed model is applied to the image labeling problem andis shown to be effective in labeling unseen images as well as reducing labelnoise in training on CIFAR-10 and MS COCO datasets.

Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks