What is meta-learning?

Meta-learning, or "learning to learn", is a subfield of machine learning where algorithms learn from previous learning experiences. Instead of training a model from scratch on each new task, meta-learning algorithms leverage knowledge gained from prior tasks to quickly adapt to new, unseen tasks with minimal training data. This is often used to accelerate the training process, improve generalization performance, and enable learning in low-data regimes. Common uses include few-shot learning, transfer learning, and reinforcement learning.