Meta-learning is a field of machine learning that aims to develop algorithms that learn from many learning tasks in order to master new tasks more quickly.
It involves not just training a model, but a learning strategy that efficiently adapts to new data.
Typical approaches include Model-Agnostic Meta-Learning (MAML), where a model is initialized so that it performs well on new tasks with only a few updates.
Meta-learning can be applied in areas such as few-shot learning or transfer learning to achieve good results with little training data.
The challenge lies in finding a balance between generalization across tasks and rapid adaptation to new data.