Model-Agnostic Meta-Learning for Fast Adaptation of …

Model-Agnostic Meta-Learning for fast Adaptation of deep Networks Chelsea Finn 1 Pieter Abbeel 1 2 Sergey Levine 1. Abstract the form of computation required to complete the task. We propose an algorithm for Meta-Learning that In this work, we propose a Meta-Learning algorithm that is Model-Agnostic , in the sense that it is com- is general and Model-Agnostic , in the sense that it can be [ ] 18 Jul 2017. patible with any model trained with gradient de- directly applied to any learning problem and model that scent and applicable to a variety of different is trained with a gradient descent procedure. Our focus learning problems, including classification, re- is on deep neural network models, but we illustrate how gression, and reinforcement learning .

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks large improvements in the task loss. The primary contribution of this work is a simple model-and task-agnostic …

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  Network, Model, Team, Learning, Deep, Fast, Adaptation, Agnostics, Model agnostic meta learning for fast adaptation, Model agnostic meta learning for fast adaptation of deep networks

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