Abstract
Neural networks are increasingly applied to real-life problems by training models for solving prediction problems with known training data. Overfitting is one of the major problems that should be considered in designing neural network models. Among various approaches to treat the overfitting problem, DropOut and DropConnect are approaches to adjust the process of training by temporarily changing the structures of neural network models. In this study, we compare the effect of applying DropOut and DropConnect approaches to a deep neural network. The result of this study will help to understand the effect of applying DropOut and DropConnect to neural networks in terms of loss and the accuracy of the trained model. It is also expected to help to design the structure of deep neural networks by effectively applying DropOut and DropConnect to control overfitting in the training of machine learning models.
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Acknowledgments
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Education) (No. NRF-2017R1D1A1B03034769).
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Lim, Hi. (2021). A Study on Comparative Analysis of the Effect of Applying DropOut and DropConnect to Deep Neural Network. In: Singh, M., Kang, DK., Lee, JH., Tiwary, U.S., Singh, D., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science(), vol 12615. Springer, Cham. https://doi.org/10.1007/978-3-030-68449-5_5
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DOI: https://doi.org/10.1007/978-3-030-68449-5_5
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