Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unbalanced GANs: Pre-training the Generator of Generative Adversarial
  Network using Variational Autoencoder   We propose Unbalanced GANs, which pre-trains the generator of the generative
adversarial network (GAN) using variational autoencoder (VAE). We guarantee the
stable training of the generator by preventing the faster convergence of the
discriminator at early epochs. Furthermore, we balance between the generator
and the discriminator at early epochs and thus maintain the stabilized training
of GANs. We apply Unbalanced GANs to well known public datasets and find that
Unbalanced GANs reduce mode collapses. We also show that Unbalanced GANs
outperform ordinary GANs in terms of stabilized learning, faster convergence
and better image quality at early epochs.

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The categories of this paper are: quant-ph
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The categories of this paper are: econ
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
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The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
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The categories of this paper are: eess
35
The categories of this paper are: mtrl-th