Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Convolutional Networks with MuxOut Layers as Multi-rate Systems for
  Image Upscaling   We interpret convolutional networks as adaptive filters and combine them with
so-called MuxOut layers to efficiently upscale low resolution images. We
formalize this interpretation by deriving a linear and space-variant structure
of a convolutional network when its activations are fixed. We introduce general
purpose algorithms to analyze a network and show its overall filter effect for
each given location. We use this analysis to evaluate two types of image
upscalers: deterministic upscalers that target the recovery of details from
original content; and second, a new generation of upscalers that can sample the
distribution of upscale aliases (images that share the same downscale version)
that look like real content.

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The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
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: chao-dyn
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The categories of this paper are: hep-lat
19
The categories of this paper are: eess
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an