Al-furas, A and AL-dosuky, M and Hamza, Taher (2018) IMPROVING FEATURE MAPS IN EARLY LAYERS OF CONVOLUTIONAL NEURAL NETWORKS USING OTSU METHOD. International Journal of Intelligent Computing and Information Sciences, 16 (2). pp. 37-45. ISSN 2535-1710
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Abstract
Abstract: A novel deep architecture Thresholding Convolution Neural Network (ThCNN) progresses in this paper; Which is a simple and effective method to regularizing features map in the early layers of Convolution Neural Network(CNN). One of the issues identified with deep learning is the features in early layers that robustness and discriminativeness. In this paper, we compute the optimal global threshold to determine the features that are passed to the next layers. We then evaluate ThCNN on an MNIST dataset comparing it CNN by applying multiple trained models. It yield decent accuracy compared to traditional CNN. It gives a 99.5%
Item Type: | Article |
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Subjects: | STM Archives > Computer Science |
Depositing User: | Unnamed user with email support@stmarchives.com |
Date Deposited: | 30 Jun 2023 05:34 |
Last Modified: | 22 Jun 2024 09:15 |
URI: | http://science.scholarsacademic.com/id/eprint/1275 |