MAML

Model-Agnostic Meta-Learning

Transfer Learning differs in that its goal is not to reuse model knowledge for new tasks, but rather to find good common initial parameters for meta

Data sampling across diverse tasks

Inner Loop: Back propagate samples for data points

Outer Loop: Compute gradient based on updated parameters for each task

Aggregate gradients from multiple tasks to update initial parameters

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning...

https://arxiv.org/abs/1703.03400

MAML

Model-Agnostic Meta-Learning

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