A discussion of migration of common neural network regularization methods on SNNs

Yilin Lyu; Bo Yin

doi:10.1117/12.3034448

16 October 2024 A discussion of migration of common neural network regularization methods on SNNs

Yilin Lyu, Bo Yin

Proceedings Volume 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024); 132915G (2024) https://doi.org/10.1117/12.3034448
Event: Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 2024, Changchun, China

Abstract

SNN models offer notable advantages in terms of memory efficiency and energy consumption when compared to traditional artificial neural network models. However, their unique impulse propagation characteristics often result in lower prediction accuracy and limited generalization abilities. Consequently, SNNs are not widely adopted in many application domains. In light of this current state of affairs, we have undertaken an investigation into the application of various dropout techniques, including standard dropout, dropout2d, Feature dropout, and alpha dropout, which are commonly employed in traditional artificial neural networks. Our objective is to assess the impact of these dropout techniques on the training loss and test accuracy of SNN models. To conduct our experiments, we have focused on fully connected SNN models using two benchmark datasets: MNIST and CIFAR-10. Our findings indicate that SNNs subjected to multiple dropout techniques exhibit subpar performance in both training loss and test accuracy evaluations. This suggests that the straightforward application of dropout and its variants does not yield the desired improvement in the performance of SNN models.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yilin Lyu and Bo Yin "A discussion of migration of common neural network regularization methods on SNNs", Proc. SPIE 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 132915G (16 October 2024); https://doi.org/10.1117/12.3034448

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Neurons

Education and training

Artificial neural networks

Neural networks

Performance modeling

Data modeling

Overfitting

Show All Keywords

Keywords/Phrases

Search In:

Publication Years