Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Spatial contrasting for deep unsupervised learning   Convolutional networks have marked their place over the last few years as the
best performing model for various visual tasks. They are, however, most suited
for supervised learning from large amounts of labeled data. Previous attempts
have been made to use unlabeled data to improve model performance by applying
unsupervised techniques. These attempts require different architectures and
training methods. In this work we present a novel approach for unsupervised
training of Convolutional networks that is based on contrasting between spatial
regions within images. This criterion can be employed within conventional
neural networks and trained using standard techniques such as SGD and
back-propagation, thus complementing supervised methods.

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The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
19
The categories of this paper are: eess
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
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The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
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The categories of this paper are: cmp-lg
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The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int