Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: User-Controllable Multi-Texture Synthesis with Generative Adversarial
  Networks   We propose a novel multi-texture synthesis model based on generative
adversarial networks (GANs) with a user-controllable mechanism. The user
control ability allows to explicitly specify the texture which should be
generated by the model. This property follows from using an encoder part which
learns a latent representation for each texture from the dataset. To ensure a
dataset coverage, we use an adversarial loss function that penalizes for
incorrect reproductions of a given texture. In experiments, we show that our
model can learn descriptive texture manifolds for large datasets and from raw
data such as a collection of high-resolution photos. Moreover, we apply our
method to produce 3D textures and show that it outperforms existing baselines.

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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas
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The categories of this paper are: plasm-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: physics
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The categories of this paper are: math
34
The categories of this paper are: chem-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: q-fin
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The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: math-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga