Zero-shot classification of small target on sea bottom using model-agnostic meta-learning

A model-agnostic meta-learning (MAML)-based active target classifier to identify small targets (e.g., mines) on the sea bottom in different ocean environments from those present in the training data is proposed. To better classify the targets deviating from those in the training set, MAML is applied to the out-of-distribution samples. Frequency-domain target and clutter scattering signals from various tasks with varying bottom types (silt/clay) and incident angles (low/moderate/high) are utilized as training data samples. MAML significantly outperforms conventional neural networks during the test. The improved generalization of MAML is explained using loss landscape in the form of a smooth convex curve.