Few-Shot Learning is a subfield of machine learning that aims to develop models that can generalize to new classes or tasks with very few training examples.
Typically, many classes with sufficient data are seen during training to learn a good representation model.
In Few-Shot Learning, the model is then expected to quickly adapt to new, previously unknown classes with only a few examples using transfer learning or meta-learning.
Well-known approaches use, for example, prototypical networks, matching networks, or model-agnostic meta-learning (MAML) to improve the ability to learn quickly.
These methods are particularly useful in areas such as image recognition, natural language processing, or robotics, where new categories often appear with little data.