Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Local Label Propagation for Large-Scale Semi-Supervised Learning   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.

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The categories of this paper are: adap-org
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: nlin
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
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The categories of this paper are: patt-sol
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The categories of this paper are: cond-mat
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The categories of this paper are: econ
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The categories of this paper are: solv-int