One-Shot Learning is a machine learning method where a model can learn and generalize from a single training example. This is in contrast to most machine learning methods which require a large number of training examples in order to learn and generalize well. One-Shot Learning is often used in situations where acquiring more training data is difficult or expensive.
Some of the emerging trends in One-Shot Learning include the use of deep learning models, the use of transfer learning, and the use of data augmentation.