CoNet: Compact and Low-Cost CNN for Image Classification

Rahadian, Fattah Azzuhry and Wahyono, Wahyono and Harjoko, Agus and Wang, Jia-Ching and Wang, Chien-Yao (2019) CoNet: Compact and Low-Cost CNN for Image Classification. In: IEEE International Conference on Consumer Electronics, 2019.

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Abstract

As the number of applications of Convolutional Neural Network increasing, the need for lightweight models to be able to run on embedded devices is also increasing. For that reason, a novel lightweight CNN is designed. Experiment on CIFAR-10 and CIFAR-100 shows that our method outperforms other state-of-the-art models with less parameters and FLOPs.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Library Dosen
Uncontrolled Keywords: Convolutional neural networks; Costs; Embedded device; Low costs; State of the art; Image classification
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Mathematics and Natural Sciences > Computer Science & Electronics Department
Depositing User: Sri JUNANDI
Date Deposited: 11 Mar 2026 01:44
Last Modified: 11 Mar 2026 01:44
URI: https://ir.lib.ugm.ac.id/id/eprint/25299

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