Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Feature Learning in Remote Sensing   The need for labeled data is among the most common and well-known practical
obstacles to deploying deep learning algorithms to solve real-world problems.
The current generation of learning algorithms requires a large volume of data
labeled according to a static and pre-defined schema. Conversely, humans can
quickly learn generalizations based on large quantities of unlabeled data, and
turn these generalizations into classifications using spontaneous labels, often
including labels not seen before. We apply a state-of-the-art unsupervised
learning algorithm to the noisy and extremely imbalanced xView data set to
train a feature extractor that adapts to several tasks: visual similarity
search that performs well on both common and rare classes; identifying outliers
within a labeled data set; and learning a natural class hierarchy
automatically.

27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
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The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
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The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
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The categories of this paper are: adap-org
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The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con