<ul><li>Adapting pre-trained deep learning models to new and unknown environments is a difficult challenge in underwater acoustic localization.</li><li>Implied uncertainty of pre-trained models is higher in environments with more mismatch between training and test data.</li><li>A method is proposed to partition test samples into certain and uncertain sets and improve labeling for uncertain samples.</li><li>The approach eliminates the need for labeled data from the target environment and results in significant improvements in model prediction accuracy.</li></ul>

Joint Source-Environment Adaptation of Data-Driven Underwater Acoustic Source Ranging Based on Model Uncertainty

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