SIESTA: Efficient Online Continual Learning with Sleep

This is a PyTorch implementation of the SIESTA algorithm from our TMLR-2023 paper. An arXiv pre-print of our paper is available.

SIESTA is a wake/sleep based online continual learning algorithm and designed to be computationally efficient for resource-constrained applications such as edge devices, mobile phones, robots, AR-VR and so on. It is capable of rapid online learning and inference while awake, but has periods of sleep where it performs offline memory consolidation.

Pre-trained MobileNetV3-L and OPQ Models

Download pre-trained MobileNetV3-L and Optimized Product Quantization (OPQ) models form this link.

Acknowledgements

Thanks for the great code base from REMIND

Citation

If using this code, please cite our paper.

@article{harun2023siesta,
title={{SIESTA}: Efficient Online Continual Learning with Sleep},
author={Md Yousuf Harun and Jhair Gallardo and Tyler L. Hayes and Ronald Kemker and Christopher Kanan},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2023},
url={https://openreview.net/forum?id=MqDVlBWRRV},
note={}
}