Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generative Adversarial Active Learning   We propose a new active learning by query synthesis approach using Generative
Adversarial Networks (GAN). Different from regular active learning, the
resulting algorithm adaptively synthesizes training instances for querying to
increase learning speed. We generate queries according to the uncertainty
principle, but our idea can work with other active learning principles. We
report results from various numerical experiments to demonstrate the
effectiveness the proposed approach. In some settings, the proposed algorithm
outperforms traditional pool-based approaches. To the best our knowledge, this
is the first active learning work using GAN.

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The categories of this paper are: bayes-an
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The categories of this paper are: physics
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The categories of this paper are: q-bio
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
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The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
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The categories of this paper are: ao-sci
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The categories of this paper are: cmp-lg
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The categories of this paper are: funct-an
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The categories of this paper are: eess
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The categories of this paper are: econ
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The categories of this paper are: math-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: mtrl-th
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The categories of this paper are: chem-ph