Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generating Image Sequence from Description with LSTM Conditional GAN   Generating images from word descriptions is a challenging task. Generative
adversarial networks(GANs) are shown to be able to generate realistic images of
real-life objects. In this paper, we propose a new neural network architecture
of LSTM Conditional Generative Adversarial Networks to generate images of
real-life objects. Our proposed model is trained on the Oxford-102 Flowers and
Caltech-UCSD Birds-200-2011 datasets. We demonstrate that our proposed model
produces the better results surpassing other state-of-art approaches.

15
The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
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The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
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The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
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The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat