A Statistical Approach to Provide Explainable Convolutional Neural Network Parameter Optimization
- DOI
- 10.2991/ijcis.d.191219.001How to use a DOI?
- Keywords
- Optimization; Convolutional neural network; Hyperparameter; Design of experiment; Taguchi; Deep learning
- Abstract
Algorithms based on convolutional neural networks (CNNs) have been great attention in image processing due to their ability to find patterns and recognize objects in a wide range of scientific and industrial applications. Finding the best network and optimizing its hyperparameters for a specific application are central challenges for CNNs. Most state-of-the-art CNNs are manually designed, while techniques for automatically finding the best architecture and hyperparameters are computationally intensive, and hence, there is a need to severely limit their search space. This paper proposes a fast statistical method for CNN parameter optimization, which can be applied in many CNN applications and provides more explainable results. The authors specifically applied Taguchi based experimental designs for network optimization in a basic network, a simplified Inception network and a simplified Resnet network, and conducted a comparison analysis to assess their respective performance and then to select the hyperparameters and networks that facilitate faster training and provide better accuracy. The results show that up to a 6% increase in classification accuracy can be achieved after parameter optimization.
- Copyright
- © 2019 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Saman Akbarzadeh AU - Selam Ahderom AU - Kamal Alameh PY - 2019 DA - 2019/12/23 TI - A Statistical Approach to Provide Explainable Convolutional Neural Network Parameter Optimization JO - International Journal of Computational Intelligence Systems SP - 1635 EP - 1648 VL - 12 IS - 2 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.191219.001 DO - 10.2991/ijcis.d.191219.001 ID - Akbarzadeh2019 ER -