@inproceedings{zintgraf2019fast,
author = {Zintgraf, Luisa and Shiarlis, Kyriacos and Kurin, Vitaly and Hofmann, Katja and Whiteson, Shimon},
title = {Fast Context Adaptation via Meta-Learning},
booktitle = {Thirty-sixth International Conference on Machine Learning (ICML)},
year = {2019},
month = {June},
abstract = {We propose CAVIA, a meta-learning method for fast adaptation that is scalable, flexible, and easy to implement. 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, the context parameters are updated with one or several gradient steps on a task-specific loss that is backpropagated through the shared part of the network. Compared to approaches that adjust all parameters on a new task (e.g., MAML), CAVIA can be scaled up to larger networks without overfitting on a single task, is easier to implement, and is more robust to the inner-loop learning rate. We show empirically that CAVIA outperforms MAML on regression, classification, and reinforcement learning problems.},
url = {http://approjects.co.za/?big=en-us/research/publication/fast-context-adaptation-via-meta-learning/},
}