Abstract
Convolutional Neural Networks (CNNs) have been explored rigorously, due to their complex image classification capabilities and applied in many real-world applications. In majority of such applications, training of CNN using a back-propagation type iterative learning has become a standard practice, but this makes training of CNN very inefficient and uncertain because of various problems such as local minima and paralysis. Other iterative and non-iterative learning including exact solution-based learning might be more efficient in terms of accuracy and certainty in training, however, potential of this type of combined learning has not been fully explored by CNN researchers. Therefore, in this paper an exact solution based new convolutional neural network architecture is proposed. In proposed architecture, a novel concept is introduced in which the weights of CNN layers are updated using iterative process for a fixed number of epochs and then the weights of fully connected layer are calculated using an exact solution process. Both iterative and calculated weights are then used for training the full architecture. The proposed approach has been evaluated on three benchmark datasets such as CIFAR-10, MNIST and Digit. The experimental results have demonstrated that the proposed approach can achieve higher accuracy than the standard CNN. Statistical significance test was carried out to prove the efficacy of proposed approach.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)
Fukushima, K.: Neocognitron: a self-organizing neural network for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193–202 (1980)
LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1, 541–551 (1989)
Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings 14th International Conference on Artificial Intelligence and Statistics, pp. 315–323 (2011)
Fabelo, H., et al.: Deep learning-based framework for in vivo identification of glioblastoma tumor using hyperspectral images of human brain. Sensors (Basel), 19(4) (2019)
Le, Z., Suganthan, P.N.: Visual tracking with convolutional random vector functional link network. IEEE Trans. Cybern. 47(10), 3243–3253 (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings International Conference on Learning Representations. http://arxiv.org/abs/1409.1556 (2014)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
Szegedy, C., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. In: British Machine Vision Conference (2014)
Liang, M., Hu, X.: Recurrent convolutional neural network for object recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3367–3375 (2015)
Notley, S., Magdon-Ismail, M.: Examining the use of neural networks for feature extraction: A comparative analysis using deep learning, support vector machines, and k-nearest neighbor classifiers. https://arxiv.org/abs/1805.02294 (2018)
Huang, F.J., LeCun, Y.: Large-scale learning with SVM and convolutional for generic object categorization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 284–291 (2006)
Niu, X.X., Suen, C.Y.: A novel hybrid CNN–SVM classifier for recognizing handwritten digits. Pattern Recogn. 45(4), 1318–1325 (2011)
Sharif Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. In: CVPR Workshops (2014)
Ren, W., Yu, Y., Zhang, J., Huang, K., Learning convolutional nonlinear features for K nearest neighbor image classification. In: Proceedings of the IEEE International Conference on Pattern Recognition (ICPR), Stockholm, Sweden, 24–28 August 2014
Sinha, T., Verma, B.: A non-iterative radial basis function based quick convolutional neural network. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2020
Pang, S., Yang, X.: Deep convolutional extreme learning machine and its application in handwritten digit classification. In: Computational Intelligence and Neuroscience, pp. 1–10 (2016)
Zheng, L., et.al.: Good practice in CNN feature transfer. arXiv:1604.00133 (2016)
Ma, C., Mu, X., Sha, D.: Multi-layers feature fusion of convolutional neural network for scene classification of remote sensing. IEEE Access 7, 121685–121694 (2019)
Passalis, N., Tefas, A.: Training lightweight deep convolutional neural networks using bag-of-features pooling. IEEE Trans. Neural Networks Learn. Syst. 30(6), 1705–1715 (2019)
Digit dataset from Matlab Toolbox
The MNIST Database of handwritten digits. http://yann.lecun.com/exdb/mnist/
CIFAR-10 dataset. https://www.cs.toronto.edu/kriz/cifar.html
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Sinha, T., Verma, B. (2020). Convolutional Neural Network Architecture with Exact Solution. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1333. Springer, Cham. https://doi.org/10.1007/978-3-030-63823-8_63
Download citation
DOI: https://doi.org/10.1007/978-3-030-63823-8_63
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63822-1
Online ISBN: 978-3-030-63823-8
eBook Packages: Computer ScienceComputer Science (R0)