Model-agnostic meta-learning for fast adaptation of deep networks

Model-agnostic meta-learning for fast adaptation of deep networks. Finn, C., Abbeel, P., & Levine, S. In International conference on machine learning, pages 1126–1135, 2017. PMLR.
abstract bibtex

This paper introduces MAML, a model-agnostic meta-learning algorithm that trains model parameters for rapid adaptation to new tasks with a small number of gradient steps and limited data.

@inproceedings{finn2017model,
  title={Model-agnostic meta-learning for fast adaptation of deep networks},
  author={Finn, Chelsea and Abbeel, Pieter and Levine, Sergey},
  booktitle={International conference on machine learning},
  pages={1126--1135},
  year={2017},
  organization={PMLR},
  abstract = {This paper introduces MAML, a model-agnostic meta-learning algorithm that trains model parameters for rapid adaptation to new tasks with a small number of gradient steps and limited data.}
}

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