A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

Abstract Data scarcity is a major challenge when training deep learning (DL) models. The survey ends with a list of applications that suffer from data scarcity, several alternatives are proposed in order to generate more data in each application including Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, and Cybersecurity.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 DL demands a large amount of data to achieve exceptional performance.
• 2 Unfortunately, many applications have small or inadequate data to train DL frameworks.
• 3 Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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