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http://dx.doi.org/10.23087/jkicsp.2022.23.3.010

HSE Block : Automatic Optimization of the Number of Convolutional Layer Filters using SE Block  

Tae-Wook Kim (Division of Software, Yonsei University)
Hyeon-Jin Jung (Department of Computer & Telecommunications Engineering, Yonsei University)
Ellen J. Hong (Division of Software, Yonsei University)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.23, no.3, 2022 , pp. 179-184 More about this Journal
Abstract
In this paper, we are going to study how we can automatically determine the number of convolutional filters for the optimal model without a search algorithm. This paper proposes HSE Block by connecting SE Block proposed in SENet to a convolutional neural network and connecting a convolutional neural network not learned at the bottom. An experiment was conducted to increase the number of filters by one per 3 epoch using two datasets for the HSEBlock model and to increase the number of filters by the value in the filter. Based on this experiment, the model was constructed with multi-layer HSE Block instead of layer HSE Block, and the experiment was carried out using a dataset that was more difficult to learn than the one used in the previous experiment. The effect of HSE Block was verified by conducting an experiment with the number of HSE Blocks set to 2, 3, 4, and 5 on a dataset that is more difficult to learn than before.
Keywords
Filter; Optimization; SENet; Convolutional Neural Network; SE Block's Excitation;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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1 Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of machine learning research, 13(2).
2 Liashchynskyi, P., & Liashchynskyi, P. (2019). Grid search, random search, genetic algorithm: A big comparison for NAS. arXiv preprint arXiv:1912.06059.
3 Hu, J., Shen, L., & Sun, G. (2018). Squeezeand-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7132-7141).
4 He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision (pp. 1026-1034).
5 Muresan, H., & Oltean, M. (2017). Fruit recognition from images using deep learning. arXiv preprint arXiv:1712.00580.
6 Li, Z., Li, F., Zhu, L., & Yue, J. (2020). Vegetable recognition and classification based on improved VGG deep learning network model. International Journal of Computational Intelligence Systems, 13(1), 559-564.   DOI
7 Roy, P., Ghosh, S., Bhattacharya, S., & Pal, U. (2018). Effects of degradations on deep neural network architectures. arXiv preprint arXiv:1807.10108.