Research compares data augmentation for sonar-based deep learning

A new research paper explores data augmentation techniques for training deep neural networks (DNNs) on Synthetic Aperture Sonar (SAS) data. The study addresses the challenge of limited labeled SAS data by comparing various augmentation strategies, including conventional image manipulations and physics-based methods. The research also examines the effectiveness of these augmentations when used with modern architectures like transformers, finding that while augmentation can improve accuracy, the benefits differ across methods and not all augmentations are helpful. AI

IMPACT This research could lead to more effective methods for training AI models on specialized sonar data, potentially improving applications in areas like underwater object detection.

RANK_REASON The cluster contains an academic paper detailing a comparison of methods for training deep neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Automatic Target Recognition
• Deep Neural Networks
• synthetic aperture sonar
• transformers

• paper
• model release

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