Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MatchingGAN: Matching-based Few-shot Image Generation   To generate new images for a given category, most deep generative models
require abundant training images from this category, which are often too
expensive to acquire. To achieve the goal of generation based on only a few
images, we propose matching-based Generative Adversarial Network (GAN) for
few-shot generation, which includes a matching generator and a matching
discriminator. Matching generator can match random vectors with a few
conditional images from the same category and generate new images for this
category based on the fused features. The matching discriminator extends
conventional GAN discriminator by matching the feature of generated image with
the fused feature of conditional images. Extensive experiments on three
datasets demonstrate the effectiveness of our proposed method.

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The categories of this paper are: hep-ex
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The categories of this paper are: atom-ph
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The categories of this paper are: stat
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The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
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The categories of this paper are: supr-con
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The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-alg
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The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: q-fin
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-ex