Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Use of Neural Signals to Evaluate the Quality of Generative Adversarial
  Network Performance in Facial Image Generation   There is a growing interest in using generative adversarial networks (GANs)
to produce image content that is indistinguishable from real images as judged
by a typical person. A number of GAN variants for this purpose have been
proposed, however, evaluating GANs performance is inherently difficult because
current methods for measuring the quality of their output are not always
consistent with what a human perceives. We propose a novel approach that
combines a brain-computer interface (BCI) with GANs to generate a measure we
call Neuroscore, which closely mirrors the behavioral ground truth measured
from participants tasked with discerning real from synthetic images. This
technique we call a neuro-AI interface, as it provides an interface between a
human's neural systems and an AI process. In this paper, we first compare the
three most widely used metrics in the literature for evaluating GANs in terms
of visual quality and compare their outputs with human judgments. Secondly we
propose and demonstrate a novel approach using neural signals and rapid serial
visual presentation (RSVP) that directly measures a human perceptual response
to facial production quality, independent of a behavioral response measurement.
The correlation between our proposed Neuroscore and human perceptual judgments
has Pearson correlation statistics: $\mathrm{r}(48) = -0.767, \mathrm{p} =
2.089e-10$. We also present the bootstrap result for the correlation i.e.,
$\mathrm{p}\leq 0.0001$. Results show that our Neuroscore is more consistent
with human judgment compared to the conventional metrics we evaluated. We
conclude that neural signals have potential applications for high quality,
rapid evaluation of GANs in the context of visual image synthesis.

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The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
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The categories of this paper are: eess
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The categories of this paper are: alg-geom
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The categories of this paper are: plasm-ph
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The categories of this paper are: stat