Ayat, Sayed Omid and Rizvi, Shahriyar Masud and Abdellatef, Hamdan and Ab. Rahman, Ab. Al-Hadi and Abdul Manan, Shahidatul Sadiah (2023) A compact spectral model for convolutional neural network. In: 7th Future Technologies Conference, FTC 2022, 20 October 2022 - 21 October 2022, Vancouver, Canada.
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Official URL: http://dx.doi.org/10.1007/978-3-031-18461-1_7
Abstract
The convolutional neural network (CNN) has gained widespread adoption in computer vision (CV) applications in recent years. However, the high computational complexity of spatial (conventional) CNNs makes real-time deployment in CV applications difficult. Spectral representation (frequency domain) is one of the most effective ways to reduce the large computational workload in CNN models, and thus beneficial for any processing platform. By reducing the size of feature maps, a compact spectral CNN model is proposed and developed in this paper by utilizing just the lower frequency components of the feature maps. When compared to similar models in the spatial domain, the proposed compact spectral CNN model achieves at least 24.11 × and 4.96 × faster classification speed on AT &T face recognition and MNIST digit/fashion classification datasets, respectively.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | convolutional neural network (CNN), spectral domain CNN |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Electrical Engineering |
| ID Code: | 108277 |
| Deposited By: | Yanti Mohd Shah |
| Deposited On: | 22 Oct 2024 07:50 |
| Last Modified: | 22 Oct 2024 07:50 |
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