Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MicroNet: Towards Image Recognition with Extremely Low FLOPs   In this paper, we present MicroNet, which is an efficient convolutional
neural network using extremely low computational cost (e.g. 6 MFLOPs on
ImageNet classification). Such a low cost network is highly desired on edge
devices, yet usually suffers from a significant performance degradation. We
handle the extremely low FLOPs based upon two design principles: (a) avoiding
the reduction of network width by lowering the node connectivity, and (b)
compensating for the reduction of network depth by introducing more complex
non-linearity per layer. Firstly, we propose Micro-Factorized convolution to
factorize both pointwise and depthwise convolutions into low rank matrices for
a good tradeoff between the number of channels and input/output connectivity.
Secondly, we propose a new activation function, named Dynamic Shift-Max, to
improve the non-linearity via maxing out multiple dynamic fusions between an
input feature map and its circular channel shift. The fusions are dynamic as
their parameters are adapted to the input. Building upon Micro-Factorized
convolution and dynamic Shift-Max, a family of MicroNets achieve a significant
performance gain over the state-of-the-art in the low FLOP regime. For
instance, MicroNet-M1 achieves 61.1% top-1 accuracy on ImageNet classification
with 12 MFLOPs, outperforming MobileNetV3 by 11.3%.

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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
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The categories of this paper are: chao-dyn
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The categories of this paper are: patt-sol
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The categories of this paper are: q-alg
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The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: stat