On Modulating the Gradient for Meta-Learning
Christian Simon, Piotr Koniusz, Richard Nock, Mehrtash Harandi
Abstract
Inspired by optimization techniques, we propose a novel meta-learning algorithm with gradient modulation to encourage fast-adaptation of neural networks in the absence of abundant data. Our method, termed ModGrad, is designed to circumvent the noisy nature of the gradients which is prevalent in low-data regimes. Furthermore and having the scalability concern in mind, we formulate ModGrad via low-rank approximations, which in turn enables us to employ ModGrad to adapt hefty neural networks. We thoroughly assess and contrast ModGrad against a large family of meta-learning techniques and observe that the proposed algorithm outperforms baselines comfortably while enjoying faster convergence."
BibTeX
@inproceedings{eccv2020_onmodulatingtheg,
title = {On Modulating the Gradient for Meta-Learning},
author = {Christian Simon and Piotr Koniusz and Richard Nock and Mehrtash Harandi},
booktitle = {ECCV 2020},
year = {2020}
}