• Journal of the Institute of Convergence Signal Processing
• /
• v.23 no.3
• /
• pp.179-184
• /
• 2022

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.