ICML 2018oral455 citations
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace
Abstract
Gradient-based meta-learning methods leverage gradient descent to learn the commonalities among various tasks. While previous such methods have been successful in meta-learning tasks, they resort to simple gradient descent during meta-testing. Our primary contribution is the
BibTeX
@InProceedings{pmlr-v80-lee18a,
title = {Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace},
author = {Lee, Yoonho and Choi, Seungjin},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
pages = {2927--2936},
year = {2018},
editor = {Dy, Jennifer and Krause, Andreas},
volume = {80},
series = {Proceedings of Machine Learning Research},
month = {10--15 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v80/lee18a/lee18a.pdf},
url = {https://proceedings.mlr.press/v80/lee18a.html},
abstract = {Gradient-based meta-learning methods leverage gradient descent to learn the commonalities among various tasks. While previous such methods have been successful in meta-learning tasks, they resort to simple gradient descent during meta-testing. Our primary contribution is the