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%
Al-furas, A., AL-dosuky, M., & Hamza, T. (2016). IMPROVING FEATURE MAPS IN EARLY LAYERS OF CONVOLUTIONAL NEURAL NETWORKS USING OTSU METHOD. International Journal of Intelligent Computing and Information Sciences, 16(2), 37-45. doi: 10.21608/ijicis.2018.10905
MLA
A Al-furas; M AL-dosuky; Taher Hamza. "IMPROVING FEATURE MAPS IN EARLY LAYERS OF CONVOLUTIONAL NEURAL NETWORKS USING OTSU METHOD". International Journal of Intelligent Computing and Information Sciences, 16, 2, 2016, 37-45. doi: 10.21608/ijicis.2018.10905
HARVARD
Al-furas, A., AL-dosuky, M., Hamza, T. (2016). '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. doi: 10.21608/ijicis.2018.10905
VANCOUVER
Al-furas, A., AL-dosuky, M., Hamza, T. IMPROVING FEATURE MAPS IN EARLY LAYERS OF CONVOLUTIONAL NEURAL NETWORKS USING OTSU METHOD. International Journal of Intelligent Computing and Information Sciences, 2016; 16(2): 37-45. doi: 10.21608/ijicis.2018.10905
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