How to Train MAML(Model-Agnostic Meta-Learning)

Author(s): Sherwin Chen

Originally published on Towards AI.

An elaborate explanation for MAML and more

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Model-Agnostic Meta-Learning(MAML) has been growing more and more popular in the field of meta-learning since it’s first introduced by Finn et al. in 2017. It is a simple, general, and effective optimization algorithm that does not place any constraints on the model architecture or loss functions. As a result, it can be combined with arbitrary networks and different types of loss functions, which makes it applicable to a variety of different learning processes.

This article consists of two parts: we first explain MAML, presenting a detailed discussion and visualizing the learning process. Then we describe some of the potential… Read the full blog for free on Medium.

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Published via Towards AI