Label distribution-guided transfer learning for underwater source localization.
J Acoust Soc Am
; 151(6): 4140, 2022 Jun.
Article
em En
| MEDLINE
| ID: mdl-35778193
ABSTRACT
Underwater source localization by deep neural networks (DNNs) is challenging since training these DNNs generally requires a large amount of experimental data and is computationally expensive. In this paper, label distribution-guided transfer learning (LD-TL) for underwater source localization is proposed, where a one-dimensional convolutional neural network (1D-CNN) is pre-trained with the simulation data generated by an underwater acoustic propagation model and then fine-tuned with a very limited amount of experimental data. In particular, the experimental data for fine-tuning the pre-trained 1D-CNN are labeled with label distribution vectors instead of one-hot encoded vectors. Experimental results show that the performance of underwater source localization with a very limited amount of experimental data is significantly improved by the proposed LD-TL.
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Base de dados:
MEDLINE
Idioma:
En
Revista:
J Acoust Soc Am
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
China