model-agnostic meta-learning

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Model-agnostic meta-learning is a general optimization-based meta-learning framework that trains models to rapidly adapt to new tasks with minimal data, regardless of the underlying model architecture.

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Chelsea Finn knownFor model-agnostic meta-learning
Chelsea Finn notableWork Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
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Prototypical Networks comparedWith MAML
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miniImageNet popularizedBy Model-Agnostic Meta-Learning (MAML)
linked to: model-agnostic meta-learning