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Symmetric learning data augmentation model for underwater target noise data expansion

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Show simple item record He, Ming Wang, Hongbin Zhou, Lianke Wang, Pengming Ju, Andrew 2019-04-17T15:40:29Z 2019-04-17T15:40:29Z 2018
dc.description peer-reviewed en_US
dc.description.abstract An important issue for deep learning models is the acquisition of training of data. Without abundant data from a real production environment for training, deep learning models would not be as widely used as they are today. However, the cost of obtaining abundant real-world environment is high, especially for underwater environments. It is more straightforward to simulate data that is closed to that from real environment. In this paper, a simple and easy symmetric learning data augmentation model (SLDAM) is proposed for underwater target radiate-noise data expansion and generation. The SLDAM, taking the optimal classifier of an initial dataset as the discriminator, makes use of the structure of the classifier to construct a symmetric generator based on antagonistic generation. It generates data similar to the initial dataset that can be used to supplement training data sets. This model has taken into consideration feature loss and sample loss function in model training, and is able to reduce the dependence of the generation and expansion on the feature set. We verified that the SLDAM is able to data expansion with low calculation complexity. Our results showed that the SLDAM is able to generate new data without compromising data recognition accuracy, for practical application in a production environment en_US
dc.language.iso eng en_US
dc.publisher Tech Science Press en_US
dc.relation.ispartofseries CRC;57 (3), pp. 521-532
dc.rights Copyright © 2018 Tech Science Press en_US
dc.subject data augmentation en_US
dc.subject symmetric learning en_US
dc.subject data expansion en_US
dc.subject underwater target noise data en_US
dc.title Symmetric learning data augmentation model for underwater target noise data expansion en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.32604/cmc.2018.03710
dc.contributor.sponsor National Natural Science Foundation of China en_US
dc.relation.projectid 61772152 en_US
dc.relation.projectid 61502037 en_US
dc.relation.projectid JCKY2016206B001 en_US
dc.relation.projectid JCKY2014206C002 en_US
dc.relation.projectid JCKY2017604C010 en_US
dc.relation.projectid JSQB2017206C002 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US

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