Optimisation of spatial-exploitation CNN models through hyperparameter-tuning and human-in-the-loop combination
by Luke Beveridge; Keshav Dahal
International Journal of Artificial Intelligence and Soft Computing (IJAISC), Vol. 8, No. 2, 2024

Abstract: Spatial-exploitation convolutional neural networks (CNNs) have a simplified architecture compared to other CNN models. However, devices with limited computational resources could struggle with processing spatial-exploitation CNNs. To address this, we investigate two methods to optimise spatial-exploitation CNN models for time efficiency and classification accuracy: hyperparameter-tuning, and human-in-the-loop (HITL). We apply grid-search to optimise the hyperparameter space, whilst HITL is used to identify whether the time-to-accuracy relationship of the optimised model can be improved. To show the versatility of combining the two methods, CIFAR-10, MNIST, and Imagenette are used as model input. This paper contributes to spatial-exploitation CNN optimisation by combining hyperparameter-tuning and HITL. Results show that this combination improves classification accuracy by 1.47-2.34% and reduces the time taken to conduct this task by 27-28%, depending on dataset. We conclude that combining hyperparameter-tuning and HITL are a viable approach to optimise spatial-exploitation CNNs for devices with limited computational resources.

Online publication date: Thu, 04-Jul-2024

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Artificial Intelligence and Soft Computing (IJAISC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com