Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Instance Map based Image Synthesis with a Denoising Generative
  Adversarial Network   Semantic layouts based Image synthesizing, which has benefited from the
success of Generative Adversarial Network (GAN), has drawn much attention in
these days. How to enhance the synthesis image equality while keeping the
stochasticity of the GAN is still a challenge. We propose a novel denoising
framework to handle this problem. The overlapped objects generation is another
challenging task when synthesizing images from a semantic layout to a realistic
RGB photo. To overcome this deficiency, we include a one-hot semantic label map
to force the generator paying more attention on the overlapped objects
generation. Furthermore, we improve the loss function of the discriminator by
considering perturb loss and cascade layer loss to guide the generation
process. We applied our methods on the Cityscapes, Facades and NYU datasets and
demonstrate the image generation ability of our model.

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The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
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The categories of this paper are: quant-ph
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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: alg-geom
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The categories of this paper are: econ
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The categories of this paper are: physics
5
The categories of this paper are: gr-qc
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The categories of this paper are: acc-phys
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The categories of this paper are: nlin
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The categories of this paper are: hep-ex
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The categories of this paper are: stat