Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Truly shift-equivariant convolutional neural networks with adaptive
  polyphase upsampling   Convolutional neural networks lack shift equivariance due to the presence of
downsampling layers. In image classification, adaptive polyphase downsampling
(APS-D) was recently proposed to make CNNs perfectly shift invariant. However,
in networks used for image reconstruction tasks, it can not by itself restore
shift equivariance. We address this problem by proposing adaptive polyphase
upsampling (APS-U), a non-linear extension of conventional upsampling, which
allows CNNs with symmetric encoder-decoder architecture (for example U-Net) to
exhibit perfect shift equivariance. With MRI and CT reconstruction experiments,
we show that networks containing APS-D/U layers exhibit state of the art
equivariance performance without sacrificing on image reconstruction quality.
In addition, unlike prior methods like data augmentation and anti-aliasing, the
gains in equivariance obtained from APS-D/U also extend to images outside the
training distribution.

35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas