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