We propose CAVIA for meta-learning, a simple extension to MAML that is less prone to meta-overfitting, easier to parallelise, and more interpretable. CAVIA partitions the model parameters into two parts: context parameters that serve as additional input to the model and are adapted on individual tasks, and shared parameters that are meta-trained and shared across tasks. At test time, only the context parameters are updated, leading to a low-dimensional task representation. We show empirically that CAVIA outperforms MAML for regression, classification, and reinforcement learning. Our experiments also highlight weaknesses in current benchmarks, in that the amount of adaptation needed in some cases is small.
@article{arxiv.1810.03642,
title = {Fast Context Adaptation via Meta-Learning},
author = {Luisa M Zintgraf and Kyriacos Shiarlis and Vitaly Kurin and Katja Hofmann and Shimon Whiteson},
journal= {arXiv preprint arXiv:1810.03642},
year = {2019}
}
Comments
Published at the International Conference on Machine Learning (ICML) 2019