An attempt of finding an appropriate number of convolutional layers in cnns based on benchmarks of heterogeneous datasets

[1] H. H. Aghdam and E. J. Heravi, Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and Classification. Cham, Switzerland: Springer, 2017. https://doi.org/10.1007/978-3-319-57550-610.1007/978-3-319-57550-6Open DOISearch in Google Scholar

[2] A. Gibson and J. Patterson, Deep Learning: A Practitioner’s Approach. O’Reilly Media, 2017.Search in Google Scholar

[3] S. Srinivas, R. K. Sarvadevabhatla, K. R. Mopuri, N. Prabhu, S. S. S. Kruthiventi, and R. V. Babu, “Chapter 2 - An Introduction to Deep Convolutional Neural Nets for Computer Vision,” in Deep Learning for Medical Image Analysis, S. K. Zhou, H. Greenspan, and D. Shen, Eds. Academic Press, 2017, pp. 25-52. https://doi.org/10.1016/b978-0-12-810408-8.00003-110.1016/b978-0-12-810408-8.00003-1Open DOISearch in Google Scholar

[4] V. Andrearczyk and P. F. Whelan, “Using Filter Banks in Convolutional Neural Networks for Texture Classification,” Pattern Recognition Letters, vol. 84, pp. 63-69, Dec. 2016. https://doi.org/10.1016/j.patrec.2016.08.01610.1016/j.patrec.2016.08.016Open DOISearch in Google Scholar

[5] Z. Liao and G. Carneiro, “A Deep Convolutional Neural Network Module that Promotes Competition of Multiple-Size Filters,” Pattern Recognition, vol. 71, pp. 94-105, 2017. https://doi.org/10.1016/j.patcog.2017.05.02410.1016/j.patcog.2017.05.024Open DOISearch in Google Scholar

[6] D. Ciresan, U. Meier, J. Masci, L. M. Gambardella, and J. Schmidhuber, “Flexible, High Performance Convolutional Neural Networks for Image Classification,” in Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 2, pp. 1237- 1242, 2011.Search in Google Scholar

[7] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification With Deep Convolutional Neural Networks,” Communications of the ACM, vol. 60, iss. 6, pp. 84-90, 2017. https://doi.org/10.1145/306538610.1145/3065386Open DOISearch in Google Scholar

[8] J. Mutch and D. G. Lowe, “Object Class Recognition and Localization Using Sparse Features With Limited Receptive Fields,” International Journal of Computer Vision, vol. 80, iss. 1, pp. 45-57, 2008. https://doi.org/10.1007/s11263-007-0118-010.1007/s11263-007-0118-0Search in Google Scholar

[9] V. V. Romanuke, “Appropriate Number and Allocation of ReLUs in Convolutional Neural Networks,” Research Bulletin of the National Technical University of Ukraine “Kyiv Polytechnic Institute”, no. 1, pp. 69-78, 2017. https://doi.org/10.20535/1810-0546.2017.1.8815610.20535/1810-0546.2017.1.88156Search in Google Scholar

[10] P. Date, J. A. Hendler, and C. D. Carothers, “Design Index for Deep Neural Networks,” Procedia Computer Science, vol. 88, pp. 131-138, 2016. https://doi.org/10.1016/j.procs.2016.07.41610.1016/j.procs.2016.07.416Search in Google Scholar

[11] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Computer Vision and Pattern Recognition, 2015.Search in Google Scholar

[12] V. V. Romanuke, “Boosting Ensembles of Heavy Two-Layer Perceptrons for Increasing Classification Accuracy in Recognizing Shifted-Turned- Scaled Flat Images With Binary Features,” Journal of Information and Organizational Sciences, vol. 39, no. 1, pp. 75-84, 2015.Search in Google Scholar

[13] V. V. Romanuke, “Two-Layer Perceptron for Classifying Flat Scaled- Turned-Shifted Objects by Additional Feature Distortions in Training,” Journal of Uncertain Systems, vol. 9, no. 4, pp. 286-305, 2015.Search in Google Scholar

[14] P. K. Rhee, E. Erdenee, S. D. Kyun, M. U. Ahmed, and S. Jin, “Active and Semi-Supervised Learning for Object Detection With Imperfect Data,” Cognitive Systems Research, vol. 45, pp. 109-123, 2017. https://doi.org/10.1016/j.cogsys.2017.05.00610.1016/j.cogsys.2017.05.006Open DOISearch in Google Scholar

[15] P. Tang, H. Wang, and S. Kwong, “G-MS2F: GoogLeNet Based Multi- Stage Feature Fusion of Deep CNN for Scene Recognition,” Neurocomputing, vol. 225, pp. 188-197, 2017. https://doi.org/10.1016/j.neucom.2016.11.02310.1016/j.neucom.2016.11.023Open DOISearch in Google Scholar

[16] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going Deeper With Convolutions,” Computer Vision and Pattern Recognition, 2014.10.1109/CVPR.2015.7298594Search in Google Scholar

[17] V. V. Romanuke, “Classifying Scaled-Turned-Shifted Objects With Optimal Pixel-to-Scale-Turn-Shift Standard Deviations Ratio in Training 2-Layer Perceptron on Scaled-Turned-Shifted 4800-Featured Objects Under Normally Distributed Feature Distortion,” Electrical, Control and Communication Engineering, vol. 13, iss. 1, pp. 45-54, 2017. https://doi.org/10.1515/ecce-2017-000710.1515/ecce-2017-0007Open DOISearch in Google Scholar

[18] V. V. Romanuke, “Classification Error Percentage Decrement of Two- Layer Perceptron for Classifying Scaled Objects on the Pattern of Monochrome 60-by-80-Images of 26 Alphabet Letters by Training With Pixel-Distorted Scaled Images,” Scientific bulletin of Chernivtsi National University of Yuriy Fedkovych. Series: Computer systems and components, vol. 4, iss. 3, pp. 53-64, 2013.Search in Google Scholar

[19] M. Sun, Z. Song, X. Jiang, J. Pan, and Y. Pang, “Learning Pooling for Convolutional Neural Network,” Neurocomputing, vol. 224, pp. 96-104, 2017. https://doi.org/10.1016/j.neucom.2016.10.04910.1016/j.neucom.2016.10.049Open DOISearch in Google Scholar

[20] D. Scherer, A. Müller, and S. Behnke, “Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition,” in International Conference on Artificial Neural Networks (ICANN 2010), pp. 92-101, 2010. https://doi.org/10.1007/978-3-642-15825-4_1010.1007/978-3-642-15825-4_10Open DOISearch in Google Scholar

[21] S. Lai, L. Jin, and W. Yang, “Toward High-Performance Online HCCR: A CNN Approach With DropDistortion, Path Signature and Spatial Stochastic Max-Pooling,” Pattern Recognition Letters, vol. 89, pp. 60-66, 2017. https://doi.org/10.1016/j.patrec.2017.02.01110.1016/j.patrec.2017.02.011Open DOISearch in Google Scholar

[22] N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks From Overfitting,” Journal of Machine Learning Research, vol. 15, pp. 1929-1958, 2014.Search in Google Scholar

[23] L. P. F. Garcia, A. C. P. L. F. de Carvalho, and A. C. Lorena, “Effect of Label Noise in the Complexity of Classification Problems,” Neurocomputing, vol. 160, pp. 108-119, 2015. https://doi.org/10.1016/j.neucom.2014.10.085.10.1016/j.neucom.2014.10.085Open DOISearch in Google Scholar