Review
Convolutional neural networks and its architecture
Keywords:
LeNet, AlexNet, ResNet, DenseNet, VGGNetAbstract
Deep Learning is-one of the machine learning areas, applied in recent areas. Various techniques have been proposed depends on varieties of learning, including unsupervised, semi-supervised, and supervised-learning. Some of the experimental results proved that the deep learning systems are performed well compared to conventional machine learning systems in image processing, computer vision and pattern recognition. This paper provides a brief survey, beginning with Deep Neural Network (DNN) in Deep Learning area. The survey moves on-the Convolutional Neural Network (CNN) and its architectures, such as LeNet, AlexNet, GoogleNet, VGG16, VGG19, Resnet50 etc. We have included transfer learning by using the CNN’s pre-trained architectures. These architectures are tested with large ImageNet data sets. The deep learning techniques are analyzed with the help of most popular data sets, which are freely available in web. Based on this survey, conclude the performance of the system depends on the GPU system.
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References
LeCun, &Yann, (1998). "Gradient-based learning applied to document recognition", Proceedings of the IEEE, IEEE. Vol. 86.11, pp. 2278-2324.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012).”Imagenet classification with deep convolutional neural networks”, In Advances in neural information processing systems, pp. 1097-1105.
He, K., Zhang, X., Ren, S., &Sun, J. (2016). “Deep residual learning for image recognition”. In Proceedings of the IEEE conference on computer vision and pattern recognition, IEEE (CVPR), pp. 770-778.
Sajja Tulasi Krishna, Hemantha Kumar Kalluri
Hana D., Qigang Liu, &Weiguo Fan. (2017), “A New Image Classification Method Using CNN transfer learning and Web Data Augmentation”, Expert Systems with Applications, Elsevier, Vol. 95, pp. 43- 56.
Simonyan, Karen, & Andrew Zisserman (1998)," Very deep convolutional networks for large-scale image recognition", arXiv preprint arXiv, pp. 1409.1556.
G.huang,Z.Liu,L.V.D matten et al”Densely connected convolutional network” 2017 IEE
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