What is few-shot learning? How does it differ from the conventional training procedure for supervised learning?
Few-shot learning is a type of supervised learning for small training sets with a very small example-to-class ratio. In regular supervised learning, we train models by iterating over a training set where the model always sees a fixed set of classes. In few-shot learning, we are working on a support set from which we create multiple training tasks to assemble training episodes, where each training task consists of different classes.
