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    Symmetric Learning Data Augmentation Model for Underwater Target Noise Data Expansion

    Ming He1,2, Hongbin Wang1,*, Lianke Zhou1, Pengming Wang3, Andrew Ju4

    CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 521-532, 2018, DOI:10.32604/cmc.2018.03710

    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… More >

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