University of Limerick
Browse
- No file added yet -

Symmetric learning data augmentation model for underwater target noise data expansion

Download (452.17 kB)
journal contribution
posted on 2019-04-17, 15:40 authored by Ming He, Hongbin Wang, Lianke Zhou, Pengming Wang, Andrew Ju
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

History

Publication

CRC;57 (3), pp. 521-532

Publisher

Tech Science Press

Note

peer-reviewed

Other Funding information

National Natural Science Foundation of China

Rights

Copyright © 2018 Tech Science Press

Language

English

Usage metrics

    University of Limerick

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC