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Authors: Taihei Asai and Koichiro Yamauchi

Affiliation: Institute of Computer Science, Chubu University, Matsumoto-cho 1200, Kasugai, Aichi, Japan

Keyword(s): Hidden Neural Network, Incremental Learning, Continual Learning, Deep Neural Networks.

Abstract: We propose a new architecture called “swap-deep neural network” that enables the learning and inference of large-scale artificial neural networks on edge devices with low power consumption and computational complexity. The proposed method is based on finding and integrating subnetworks from randomly initialized networks for each incremental learning phase. We demonstrate that our method achieves a performance equivalent to that of conventional deep neural networks for a variety of various classification tasks.

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Paper citation in several formats:
Asai, T. and Yamauchi, K. (2024). Swap-Deep Neural Network: Incremental Inference and Learning for Embedded Systems. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-684-2; ISSN 2184-4313, SciTePress, pages 418-427. DOI: 10.5220/0012465500003654

@conference{icpram24,
author={Taihei Asai. and Koichiro Yamauchi.},
title={Swap-Deep Neural Network: Incremental Inference and Learning for Embedded Systems},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2024},
pages={418-427},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012465500003654},
isbn={978-989-758-684-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Swap-Deep Neural Network: Incremental Inference and Learning for Embedded Systems
SN - 978-989-758-684-2
IS - 2184-4313
AU - Asai, T.
AU - Yamauchi, K.
PY - 2024
SP - 418
EP - 427
DO - 10.5220/0012465500003654
PB - SciTePress