@inproceedings{
demeule2023adaptive,
title={Adaptive Resolution Residual Networks},
author={L{\'e}a Demeule and Mahtab Sandhu and Glen Berseth},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=9hcsB4oYxG}
}
We introduce Adaptive Resolution Residual Networks (ARRNs), a form of neural operator that enables the creation of networks for signal-based tasks that can be rediscretized to suit any signal resolution. ARRNs are composed of a chain of Laplacian residuals that each contain ordinary layers, which do not need to be rediscretizable for the whole network to be rediscretizable. ARRNs have the property of requiring a lower number of Laplacian residuals for exact evaluation on lower-resolution signals, which greatly reduces computational cost. ARRNs also implement Laplacian dropout, which encourages networks to become robust to low-bandwidth signals. ARRNs can thus be trained once at high-resolution and then be rediscretized on the fly at a suitable resolution with great robustness.
Adaptive Resolution Residual Networks
Léa Demeule Mahtab Sandhu Glen Berseth