  A significant issue in training deep neural networks to solve supervised
learning tasks is the need for large numbers of labelled datapoints. The goal
of semi-supervised learning is to leverage ubiquitous unlabelled data, together
with small quantities of labelled data, to achieve high task performance.
Though substantial recent progress has been made in developing semi-supervised
algorithms that are effective for comparatively small datasets, many of these
techniques do not scale readily to the large (unlaballed) datasets
characteristic of real-world applications. In this paper we introduce a novel
approach to scalable semi-supervised learning, called Local Label Propagation
(LLP). Extending ideas from recent work on unsupervised embedding learning, LLP
first embeds datapoints, labelled and otherwise, in a common latent space using
a deep neural network. It then propagates pseudolabels from known to unknown
datapoints in a manner that depends on the local geometry of the embedding,
taking into account both inter-point distance and local data density as a
weighting on propagation likelihood. The parameters of the deep embedding are
then trained to simultaneously maximize pseudolabel categorization performance
as well as a metric of the clustering of datapoints within each psuedo-label
group, iteratively alternating stages of network training and label
propagation. We illustrate the utility of the LLP method on the ImageNet
dataset, achieving results that outperform previous state-of-the-art scalable
semi-supervised learning algorithms by large margins, consistently across a
wide variety of training regimes. We also show that the feature representation
learned with LLP transfers well to scene recognition in the Places 205 dataset.
