Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific
  Delta   Learning to generate new images for a novel category based on only a few
images, named as few-shot image generation, has attracted increasing research
interest. Several state-of-the-art works have yielded impressive results, but
the diversity is still limited. In this work, we propose a novel Delta
Generative Adversarial Network (DeltaGAN), which consists of a reconstruction
subnetwork and a generation subnetwork. The reconstruction subnetwork captures
intra-category transformation, i.e., "delta", between same-category pairs. The
generation subnetwork generates sample-specific "delta" for an input image,
which is combined with this input image to generate a new image within the same
category. Besides, an adversarial delta matching loss is designed to link the
above two subnetworks together. Extensive experiments on five few-shot image
datasets demonstrate the effectiveness of our proposed method.

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The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
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The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: supr-con
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The categories of this paper are: mtrl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: gr-qc
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The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
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The categories of this paper are: plasm-ph
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The categories of this paper are: stat