Abstract:Machine learning (ML)-based approaches are desirable for discriminating targets from clutter signals to enhance the performance of active sonar systems. However, a small dataset and imbalanced data samples between the target and clutter hinder ML applications in active sonar classification. Anomaly detection (AD), which effectively exploits the imbalance, is adopted to enhance the generalization of ML-based active sonar classifiers for small and imbalanced datasets. Generally, deep AD focuses on learning a rep… Show more
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