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A Brief Journey of Convolutional Neural Networks from 2012 to 2017
With the recent advancements in the field of Deep Learning, largely due to the increase in computational power, the long-standing problem of large-scale image classification has been solved up to a considerable threshold. Since the advent of AlexNet in 2012, there has been an increase in the research and development of various Convolutional Neural Network based model to solve problems in the field of Computer Vision. This may be primarily attributed to the success of AlexNet in successfully classifying 1000 class subset of ImageNet with considerable accuracy. Consequently, many more Convolutional Neural Networks were introduced including VGG Net, Inception Net, ResNet, DenseNet, etc. each with better performance than its predecessor. This paper serves as a brief guide to the architecture of ResNet and some of its predecessors. It will also discuss DenseNet and ResNeXt as improvements of ResNet architecture.
Computer Vision, Convolutional Neural Network, Deep learning. Image classification, ResNet, AlexNet.
- Samuel, L., Artificial Intelligence: A Frontier of Automation, The Annals of the American Academy of Political and Social Science, Vol. 340, No. 1, pp. 10-20,1962.
- Howard, J. and Gugger, S., Deep Learning for Coders with Fastai and Pytorch, O'Reilly Media Inc., 2020.
- Krizhevsky, A., One Weird Trick for Parallelizing Convolutional Neural Networks, 2012. https://doi.org/10.48550/ arXiv. 1404.5997.
- Simonyan, K. and Zisserman, A., Very Deep Convolutional Networks for Large- Scale Image Recognition, arXiv: 1409. 1556,2014.
- Szegedy, C., Liu, W., Jia, Y, Sermanet, R, Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. and Rabinovich, A., Going deeper with convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-9,2015.
- Kaiming, H., Xiangyu, Z., Shaoqing, R. and Jian, S., Deep Residual Learning for Image Recognition, IEEE Conference on Computer Vision and Pattern Recognition, pp.770-778,2016.
- Huang, G., Liu, Z., and Weinberger, K.Q., Densely Connected Convolutional Networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.2261-2269,2017.
- Xie, S., Girshick, R.B., Dollar, P., Tu, Z. and He, K., Aggregated Residual Transformations for Deep Neural Networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.5987-5995,2017.
- Khvostikov, A., Aderghal, K., Benois- Pineau, J., Krylov, A. and Catheline, G., 3D CNN-Based Classification Using sMRI and MD-DTI Images for Alzheimer Disease Studies, Computer Vision and Pattern Recognition, 2018. https://arxiv.org/abs/ 1801.05968
- Gopalakrishnana, K., Khaitan, S. K., Choudhary, A. and Agrawal, A., Deep Convolutional Neural Networks with Transfer Learning for Computer Vision- Based Data-Driven Pavement Distress Detection, Construction and Building Materials, Vol. 157, pp.322-330,2017.
- Abhi, S., What is the Differences between Artificial Neural Network (Computer Science) and Biological Neural Network, 2021. https://www.quora.com/What-is-the- differences-between-artificial-neural- network-computer-science-and-biological- neural-network, date of access: 14/03/ 2021.
- Srivastava, T, How Does Artificial Neural Network (ANN) Algorithm Work? Simplified!, https://www.scribd.com/ document/ 365270266/How-Does-Artifical-Neural- Network-ANN-Works-Simplified, 2014, date of access: 14/03/2021.
- Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., Essen, B. C. V.,Awwal,A.A. S. andAsari, V.K., The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches, Deep Learning Monitor, 2018. https://deeplearn.org/arxiv/46836/the- history-began-from-alexnet:-a-comprehen sive-survey-on-deep-learning-approaches
- Widrow, B. and Lehr, M.A., 30 Years of Adaptive Neural Networks: Perceptron, Madaline, and Backpropagation, Proceedings of the IEEE, Vol. 78, No.9,1990.
- McCulloch, W. S. and Pitts, W., A Logical Calculus of the Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics, Vol. 5, pp.115-133,1943.
- Werbos, P., Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences, Ph.D. Thesis Submitted to Applied Mathematics Depart-ment, Harvard University, 1974.
- Fukushima, K., Neocognitron: A Hierarchical Neural Network Capable of Visual Pattern Recognition, Neural Net-works, Vol. 1, No.2, pp. 119-130,1988.
- Lecun, Y, Bottou, L., Bengio, Y. and Haffner, P., Gradient-Based Learning Applied To Document Recognition, Proceedings of the IEEE, Vol. 86, No.11, pp.2278- 2324,1998.
- Anderson, J.A., Mathematical Biosciences, Vol. 14, No. 3-4, pp.197-395,1972.
- Kohonen, T., Lehtio, P., Rovamo, J., Hyvarinen, J., Bry, K. and Vainio, L., A Principle of Neural Associative Memory, Neuroscience, Vol. 2, No. 6, pp.1065-1076, 1977.
- Chaganti, S.Y., Nanda, I., Pandi, K.R., Prudhvith, T.G.N.R.S.N. and Kumar, N., Image Classification Using SVM and CNN, 2020 International Conference on Computer Science, Engineering and Applications, pp.1-5,2020.
- Hussain, M., Bird, J.J. and Faria, D.R., A Study on CNN Transfer Learning for Image Classification, Advances in Computational Intelligence Systems, pp. 191-202,2019.
- Joseph, R. and Farhadi, A., Yolov3: An Incremental Improvement, pp.1-6,2018.
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