TY - GEN

T1 - On feature normalization and data augmentation

AU - Li, Boyi

AU - Wu, Felix

AU - Lim, Ser Nam

AU - Belongie, Serge

AU - Weinberger, Kilian Q.

N1 - Funding Information: This research is supported in part by the grants from Facebook, DARPA, 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. Facebook has no collaboration with the other sponsors of this project. In particular, we appreciate the valuable discussion with Gao Huang. Funding Information: This research is supportedin part by the grants from Facebook, DARPA, theNationalScienceFoundation(III-1618134,III-1526012,IIS1149882,IIS-1724282,and TRIPODS-1740822), the Office of Naval Research DOD (N00014-17-1-2175), BillandMelindaGatesFoundation. We are thankful for generous support by Zillow and SAP AmericaInc. Facebookhasnocollaborationwiththeother sponsorsofthisproject. Inparticular, weappreciatethe valuable discussionwithGaoHuang. Publisher Copyright: © 2021 IEEE

PY - 2021

Y1 - 2021

N2 - The moments (a.k.a., mean and standard deviation) of latent features are often removed as noise when training image recognition models, to increase stability and reduce training time. However, in the field of image generation, the moments play a much more central role. Studies have shown that the moments extracted from instance normalization and positional normalization can roughly capture style and shape information of an image. Instead of being discarded, these moments are instrumental to the generation process. In this paper we propose Moment Exchange, an implicit data augmentation method that encourages the model to utilize the moment information also for recognition models. Specifically, we replace the moments of the learned features of one training image by those of another, and also interpolate the target labels-forcing the model to extract training signal from the moments in addition to the normalized features. As our approach is fast, operates entirely in feature space, and mixes different signals than prior methods, one can effectively combine it with existing augmentation approaches. We demonstrate its efficacy across several recognition benchmark data sets where it improves the generalization capability of highly competitive baseline networks with remarkable consistency.

AB - The moments (a.k.a., mean and standard deviation) of latent features are often removed as noise when training image recognition models, to increase stability and reduce training time. However, in the field of image generation, the moments play a much more central role. Studies have shown that the moments extracted from instance normalization and positional normalization can roughly capture style and shape information of an image. Instead of being discarded, these moments are instrumental to the generation process. In this paper we propose Moment Exchange, an implicit data augmentation method that encourages the model to utilize the moment information also for recognition models. Specifically, we replace the moments of the learned features of one training image by those of another, and also interpolate the target labels-forcing the model to extract training signal from the moments in addition to the normalized features. As our approach is fast, operates entirely in feature space, and mixes different signals than prior methods, one can effectively combine it with existing augmentation approaches. We demonstrate its efficacy across several recognition benchmark data sets where it improves the generalization capability of highly competitive baseline networks with remarkable consistency.

U2 - 10.1109/CVPR46437.2021.01220

DO - 10.1109/CVPR46437.2021.01220

M3 - Conference article

AN - SCOPUS:85123224319

SP - 12378

EP - 12387

JO - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings

JF - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings

SN - 1063-6919

T2 - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021

Y2 - 19 June 2021 through 25 June 2021

ER -