Small Datasets

Research on small datasets focuses on developing machine learning models that achieve high performance despite limited training data, a common constraint across many scientific domains. Current efforts concentrate on techniques like data augmentation (including extrapolation and generative methods), transfer learning, and the adaptation of model architectures such as Vision Transformers and convolutional neural networks for improved efficiency and robustness with small datasets. These advancements are crucial for accelerating scientific discovery and enabling practical applications in fields where large datasets are expensive or impossible to obtain, such as materials science, medical imaging, and precision agriculture.