Earticle

초록

영어

The Convolutional Neural Network (CNN) has shown an excellent performance in computer vision task. Applications of CNN include image classification, object detection in images, autonomous driving, etc. This paper will evaluate the performance of CNN model with ReLU and SELU as activation function. The evaluation will be performed on four different choices of hyperparameter which are initialization method, network configuration, optimization technique, and regularization. We did experiment on each choice of hyperparameter and show how it influences the network convergence and test accuracy. In this experiment, we also discover performance improvement when using SELU as activation function over ReLU.

목차

Abstract
 1. Introduction
 2. Activation function
 3. Experiments
  3.1 Initialization method
  3.2 Network configuration
  3.3 Optimization techniques
  3.4 Regularization
 4. Conclusion
 Acknowledgement
 References

키워드

저자

• Pratama Kevin [ Department of Computer Engineering, Dongseo University, Busan, Korea ]
• Dae-Ki Kang [ Department of Ubiquitous IT, Graduate School, Dongseo University, Busan, Korea ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380