Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Generation of Continuous Images   In most existing learning systems, images are typically viewed as 2D pixel
arrays. However, in another paradigm gaining popularity, a 2D image is
represented as an implicit neural representation (INR) - an MLP that predicts
an RGB pixel value given its (x,y) coordinate. In this paper, we propose two
novel architectural techniques for building INR-based image decoders:
factorized multiplicative modulation and multi-scale INRs, and use them to
build a state-of-the-art continuous image GAN. Previous attempts to adapt INRs
for image generation were limited to MNIST-like datasets and do not scale to
complex real-world data. Our proposed INR-GAN architecture improves the
performance of continuous image generators by several times, greatly reducing
the gap between continuous image GANs and pixel-based ones. Apart from that, we
explore several exciting properties of the INR-based decoders, like
out-of-the-box superresolution, meaningful image-space interpolation,
accelerated inference of low-resolution images, an ability to extrapolate
outside of image boundaries, and strong geometric prior. The project page is
located at https://universome.github.io/inr-gan.

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The categories of this paper are: cond-mat
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The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
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The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
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The categories of this paper are: solv-int
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The categories of this paper are: bayes-an
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The categories of this paper are: astro-ph
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The categories of this paper are: stat
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The categories of this paper are: mtrl-th
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The categories of this paper are: comp-gas
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: q-bio