Few-shot Learning For Joint Model In Underwater Acoustic Target Recognition

This article introduces a learning framework for underwater acoustic target recognition model with few samples. 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. 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.