MetaLearner

MetaLearner is a component in meta-learning frameworks that learns how to learn. It is a higher-level model or algorithm that observes the performance of a base learner across tasks drawn from a task distribution and outputs updates, hyperparameters, or adaptation strategies to improve future learning on new tasks. The base learner is task-specific and is trained on individual tasks while the meta-learner’s updates are learned across many tasks.

In typical gradient-based meta-learning, the meta-learner optimizes the initial parameters of the base learner so that a small number of gradient steps yields good task performance (as in iterative methods like MAML). In optimization-based approaches, the meta-learner can be a neural network that generates parameter updates, learning rates, or other optimization rules. Other interpretations include meta-learners that learn similarity metrics or decision rules used to combine or select among multiple base learners. The training regime often features inner-loop adaptation on each task and an outer-loop evaluation across tasks, with backpropagation through the adaptation process to refine the meta-learner.

Applications for meta-learners span few-shot or rapid adaptation in computer vision, natural language processing, robotics, and healthcare. Key challenges include computational efficiency, generalization to out-of-distribution tasks, and avoiding overfitting to the training task distribution.