Completion-Attention Ladder Network for Few-Shot Underwater Acoustic Recognition

Xue Lingzhi, Zeng Xiangyang, Yan Xiang, Yang Shuang

科研成果: 期刊稿件文章同行评审

4 引用 (Scopus)

摘要

Underwater acoustic object recognition is becoming attractive given the critical information available. However, this comes at the expense of large-scale annotated data, which is expensive to collect and annotate. This paper proposes a semi-supervised learning approach of CALNet to recognize insufficient sample underwater acoustic targets. Given this goal, we introduce the CALNet network containing supervised and unsupervised modules. Firstly, we leverage the supervised module to recognize the labeled signals and reduce the dimensional feature extraction of unlabeled samples. Then, the unsupervised network is designed as an auxiliary network to optimize the supervised network, which uses low-dimensional features to restore high-dimensional features of unlabeled samples to enhance the classification ability of the supervised network. We especially introduce ReLU activation function to connect the supervised and unsupervised modules that can help find a balanced relationship between classification and regression tasks for recognizing underwater acoustic signals. Extensive experiments on multiple benchmark datasets demonstrate the superiority of our framework showing that the proposed approach achieves the best recognition accuracy compared with the other approaches with few samples. Moreover, the experimental results can demonstrate the optimal combination of variables for the recognition effect of the proposed method under multiple variables.

源语言英语
页(从-至)9563-9579
页数17
期刊Neural Processing Letters
55
7
DOI
出版状态已出版 - 12月 2023

指纹

探究 'Completion-Attention Ladder Network for Few-Shot Underwater Acoustic Recognition' 的科研主题。它们共同构成独一无二的指纹。

引用此