Advancements in Image Classification using Convolutional Neural Network
arXiv:1905.03288 · doi:10.1109/ICRCICN.2018.8718718
Abstract
Convolutional Neural Network (CNN) is the state-of-the-art for image classification task. Here we have briefly discussed different components of CNN. In this paper, We have explained different CNN architectures for image classification. Through this paper, we have shown advancements in CNN from LeNet-5 to latest SENet model. We have discussed the model description and training details of each model. We have also drawn a comparison among those models.
9 pages, 15 figures, 3 Tables. Submitted to 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks(ICRCICN 2018)
References in corpus (7)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- Improving neural networks by preventing co-adaptation of feature detectors
- Squeeze-and-Excitation Networks
- Densely Connected Convolutional Networks
- ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
- Aggregated Residual Transformations for Deep Neural Networks
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