How small datasets drive efficiency in vision models

Generally, a machine learning model requires a significant amount of training data to learn to recognise patterns. However, acquiring and processing swathes of data is no small task due to many reasons, including data regulations around privacy and safety, or time and resource constraints. Nevertheless, ML models, especially vision models, can learn effectively from small datasets. Few-shot learning (FSL) is a great example, where researchers have received 70% accuracy for an image classification task by using only four samples per class. N-shot learning can be used in computer vision, NLP, healthcare, and IoT applications.