Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CrossNorm and SelfNorm for Generalization under Distribution Shifts   Traditional normalization techniques (e.g., Batch Normalization and Instance
Normalization) generally and simplistically assume that training and test data
follow the same distribution. As distribution shifts are inevitable in
real-world applications, well-trained models with previous normalization
methods can perform badly in new environments. Can we develop new normalization
methods to improve generalization robustness under distribution shifts? In this
paper, we answer the question by proposing CrossNorm and SelfNorm. CrossNorm
exchanges channel-wise mean and variance between feature maps to enlarge
training distribution, while SelfNorm uses attention to recalibrate the
statistics to bridge gaps between training and test distributions. CrossNorm
and SelfNorm can complement each other, though exploring different directions
in statistics usage. Extensive experiments on different fields (vision and
language), tasks (classification and segmentation), settings (supervised and
semi-supervised), and distribution shift types (synthetic and natural) show the
effectiveness. Code is available at
https://github.com/amazon-research/crossnorm-selfnorm

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The categories of this paper are: astro-ph
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: math
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The categories of this paper are: econ
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The categories of this paper are: comp-gas
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The categories of this paper are: q-alg
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The categories of this paper are: mtrl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-th
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The categories of this paper are: acc-phys
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The categories of this paper are: solv-int
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The categories of this paper are: hep-lat
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The categories of this paper are: chao-dyn