Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GM Score: Incorporating inter-class and intra-class generator diversity,
  discriminability of disentangled representation, and sample fidelity for
  evaluating GANs   While generative adversarial networks (GAN) are popular for their higher
sample quality as opposed to other generative models like the variational
autoencoders (VAE) and Boltzmann machines, they suffer from the same difficulty
of the evaluation of generated samples. Various aspects must be kept in mind,
such as the quality of generated samples, the diversity of classes (within a
class and among classes), the use of disentangled latent spaces, agreement of
said evaluation metric with human perception, etc. In this paper, we propose a
new score, namely, GM Score, which takes into various factors such as sample
quality, disentangled representation, intra-class and inter-class diversity,
and other metrics such as precision, recall, and F1 score are employed for
discriminability of latent space of deep belief network (DBN) and restricted
Boltzmann machine (RBM). The evaluation is done for different GANs (GAN, DCGAN,
BiGAN, CGAN, CoupledGAN, LSGAN, SGAN, WGAN, and WGAN Improved) trained on the
benchmark MNIST dataset.

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The categories of this paper are: plasm-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: adap-org
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The categories of this paper are: physics
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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: eess
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-ex
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The categories of this paper are: nlin
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The categories of this paper are: funct-an
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The categories of this paper are: acc-phys
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The categories of this paper are: gr-qc
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The categories of this paper are: solv-int
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The categories of this paper are: astro-ph
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The categories of this paper are: q-fin