Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training Generative Adversarial Networks with Limited Data   Training generative adversarial networks (GAN) using too little data
typically leads to discriminator overfitting, causing training to diverge. We
propose an adaptive discriminator augmentation mechanism that significantly
stabilizes training in limited data regimes. The approach does not require
changes to loss functions or network architectures, and is applicable both when
training from scratch and when fine-tuning an existing GAN on another dataset.
We demonstrate, on several datasets, that good results are now possible using
only a few thousand training images, often matching StyleGAN2 results with an
order of magnitude fewer images. We expect this to open up new application
domains for GANs. We also find that the widely used CIFAR-10 is, in fact, a
limited data benchmark, and improve the record FID from 5.59 to 2.42.

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The categories of this paper are: hep-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: gr-qc
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The categories of this paper are: cond-mat
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The categories of this paper are: astro-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: math-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: econ
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The categories of this paper are: adap-org
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The categories of this paper are: cmp-lg
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The categories of this paper are: comp-gas
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The categories of this paper are: chem-ph