Meta-Learning Task

A Meta-Learning Task is a machine learning task that refers to the problem of training models to learn how to learn, by generalizing across a distribution of learning tasks.

• AKA: Meta-Learning Task, Learning-to-Learn Task, Few-Shot Generalization Task.
• Context:
  • Task Input: A distribution of task datasets (each with support and query sets).
    • Optional Input: Task-specific hyperparameters, base learner configuration.
  • Task Output: A learning algorithm or model that performs well on unseen tasks.
  • Task Performance Measures: Accuracy on query sets after adaptation, generalization gap, few-shot test error.
  • Task Objective: To optimize the learning process so the resulting model adapts quickly and accurately to new tasks.
  • It can be systematically solved and automated by a metalearning system.
  • It can simulate the process of learning to adapt by training over multiple meta-train tasks.
  • It can be instantiated through N-way K-shot learning, supervised regression tasks, or reinforcement learning environments.
  • It can assess how quickly and accurately a system can generalize with limited data from novel tasks.
  • ...
• Example(s):
  • Few-shot image classification task, which requires learning from only a few labeled examples per class.
  • Meta-reinforcement learning task, where the agent must learn to solve new MDPs using prior episodes.
  • Few-shot NLP task, such as slot-filling with minimal annotated samples.
  • ...
• Counter-Example(s):
  • Single-task learning problems, which do not involve task generalization.
  • Transfer learning tasks, which rely on a fixed source-target task pair rather than a task distribution.
  • ...
• See: Few-shot learning task, Multi-task learning task, Transfer learning, Metalearning System.

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