In underwater acoustics, deep learning may improve sonar systems to help detect ships and submarines in distress or in restricted waters. However, noise interference can be a challenge. Researchers now explore an attention-based deep neural network to tackle this problem. They tested two ships, comparing their results with a typical deep neural network, and found the ABNN increases its predictions considerably as it gravitates toward the features closely correlated with the training goals.