Positional normalization
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Positional normalization. / Li, Boyi; Wu, Felix; Weinberger, Kilian Q.; Belongie, Serge.
In: Advances in Neural Information Processing Systems, Vol. 32, 2019.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Positional normalization
AU - Li, Boyi
AU - Wu, Felix
AU - Weinberger, Kilian Q.
AU - Belongie, Serge
N1 - Funding Information: This research is supported in part by the grants from Facebook, the National Science Foundation (III-1618134, III-1526012, IIS1149882, IIS-1724282, and TRIPODS-1740822), the Office of Naval Research DOD (N00014-17-1-2175), Bill and Melinda Gates Foundation. We are thankful for generous support by Zillow and SAP America Inc. Publisher Copyright: © 2019 Neural information processing systems foundation. All rights reserved.
PY - 2019
Y1 - 2019
N2 - A popular method to reduce the training time of deep neural networks is to normalize activations at each layer. Although various normalization schemes have been proposed, they all follow a common theme: normalize across spatial dimensions and discard the extracted statistics. In this paper, we propose an alternative normalization method that noticeably departs from this convention and normalizes exclusively across channels. We argue that the channel dimension is naturally appealing as it allows us to extract the first and second moments of features extracted at a particular image position. These moments capture structural information about the input image and extracted features, which opens a new avenue along which a network can benefit from feature normalization: Instead of disregarding the normalization constants, we propose to re-inject them into later layers to preserve or transfer structural information in generative networks.
AB - A popular method to reduce the training time of deep neural networks is to normalize activations at each layer. Although various normalization schemes have been proposed, they all follow a common theme: normalize across spatial dimensions and discard the extracted statistics. In this paper, we propose an alternative normalization method that noticeably departs from this convention and normalizes exclusively across channels. We argue that the channel dimension is naturally appealing as it allows us to extract the first and second moments of features extracted at a particular image position. These moments capture structural information about the input image and extracted features, which opens a new avenue along which a network can benefit from feature normalization: Instead of disregarding the normalization constants, we propose to re-inject them into later layers to preserve or transfer structural information in generative networks.
UR - http://www.scopus.com/inward/record.url?scp=85089145520&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85089145520
VL - 32
JO - Advances in Neural Information Processing Systems
JF - Advances in Neural Information Processing Systems
SN - 1049-5258
T2 - 33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019
Y2 - 8 December 2019 through 14 December 2019
ER -
ID: 301823632