Meta-Learning

Meta-learning, or learning to learn, focuses on developing algorithms that can adapt to new tasks quickly by leveraging experience from previous learning tasks. It involves learning optimal learning strategies, hyperparameters, and model architectures. Key approaches include metric-based, model-based, and optimization-based meta-learning.

Few-shot learning in computer vision, Rapid adaptation of robots to new tasks, Automated hyperparameter optimization in machine learning pipelines