Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions   Neural networks rely on convolutions to aggregate spatial information.
However, spatial convolutions are expensive in terms of model size and
computation, both of which grow quadratically with respect to kernel size. In
this paper, we present a parameter-free, FLOP-free "shift" operation as an
alternative to spatial convolutions. We fuse shifts and point-wise convolutions
to construct end-to-end trainable shift-based modules, with a hyperparameter
characterizing the tradeoff between accuracy and efficiency. To demonstrate the
operation's efficacy, we replace ResNet's 3x3 convolutions with shift-based
modules for improved CIFAR10 and CIFAR100 accuracy using 60% fewer parameters;
we additionally demonstrate the operation's resilience to parameter reduction
on ImageNet, outperforming ResNet family members. We finally show the shift
operation's applicability across domains, achieving strong performance with
fewer parameters on classification, face verification and style transfer.

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The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an