Small Scale Datasets

Research on small-scale datasets focuses on developing methods to effectively train machine learning models, particularly deep learning models like Vision Transformers and convolutional neural networks, when limited training data is available. Current efforts concentrate on techniques such as dataset condensation, self-supervised learning, transfer learning, and the development of novel architectures optimized for low-data regimes. This research is crucial because many real-world applications lack large, labeled datasets, hindering the deployment of powerful machine learning solutions; overcoming this limitation has significant implications for various fields, including drug discovery, remote sensing, and malware detection.