Dear Colleagues,

Deep learning, which has overcome many drawbacks of traditional machine learning techniques, is an alternative approach applied to many challenging real-world environments, ranging from medical imaging to agriculture to bioinformatics. Despite its great efficiency in dealing with challenging tasks, it still has some pitfalls that must be addressed, such as the lack of training data and imbalanced data. Deep learning requires a large number of data to perform well.

However, many fields suffer from a lack of sufficient data for training deep learning models because this requires a long collection process and manual labeling by human annotators, which are time-consuming and expensive procedures.

This Special Issue aims to present novel works proposing new tools and techniques to deal with data scarcity in several research areas, including different transfer learning types, physics-informed neural networks, generative adversarial networks, deep synthetic minority oversampling techniques, and model complexity.

High-quality reviews and survey papers are welcome. Papers may focus on, but are limited to, the following areas:

• Deep learning;
• Data scarcity;
• Machine learning;
• Convolutional neural network (CNN);
• Deep neural network architectures;
• Lack of training data;
• Small datasets;
• Transfer learning;
• Physics-informed neural network;
• Generative adversarial networks;
• Deep synthetic minority oversampling technique;
• Model complexity;
• Deep learning applications;
• Image classification;
• Image segmentation;
• Image registration;
• Supervised learning;
• Unsupervised learning;
• Hardware solutions;
• Overfitting;
• Imbalanced data.

Dr. Laith Alzubaid
Prof. Dr. YuanTong Gu
Guest Editors

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