Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Performance Evaluation of Deep Generative Models for Generating
  Hand-Written Character Images   There have been many work in the literature on generation of various kinds of
images such as Hand-Written characters (MNIST dataset), scene images (CIFAR-10
dataset), various objects images (ImageNet dataset), road signboard images
(SVHN dataset) etc. Unfortunately, there have been very limited amount of work
done in the domain of document image processing. Automatic image generation can
lead to the enormous increase of labeled datasets with the help of only limited
amount of labeled data. Various kinds of Deep generative models can be
primarily divided into two categories. First category is auto-encoder (AE) and
the second one is Generative Adversarial Networks (GANs). In this paper, we
have evaluated various kinds of AE as well as GANs and have compared their
performances on hand-written digits dataset (MNIST) and also on historical
hand-written character dataset of Indonesian BALI language. Moreover, these
generated characters are recognized by using character recognition tool for
calculating the statistical performance of these generated characters with
respect to original character images.

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The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
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The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
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The categories of this paper are: q-fin
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The categories of this paper are: patt-sol
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The categories of this paper are: supr-con
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The categories of this paper are: quant-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: astro-ph
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The categories of this paper are: q-bio