Few-shot learning for joint model in underwater acoustic target recognition.

In underwater acoustic target recognition, there is a lack of massive high-quality labeled samples to train robust deep neural networks, and it is difficult to collect and annotate a large amount of base class data in advance unlike the image recognition field. Therefore, conventional few-shot learning methods are difficult to apply in underwater acoustic target recognition. In this report, following advanced self-supervised learning frameworks, a learning framework for underwater acoustic target recognition model with few samples is proposed. Meanwhile, a semi-supervised fine-tuning method is proposed to improve the fine-tuning performance by mining and labeling partial unlabeled samples based on the similarity of deep features. A set of small sample datasets with different amounts of labeled data are constructed, and the performance baselines of four underwater acoustic target recognition models are established based on these datasets. Compared with the baselines, using the proposed framework effectively improves the recognition effect of four models. Especially for the joint model, the recognition accuracy has increased by 2.04% to 12.14% compared with the baselines. The model performance on only 10 percent of the labeled data can exceed that on the full dataset, effectively reducing the dependence of model on the number of labeled samples. The problem of lack of labeled samples in underwater acoustic target recognition is alleviated.