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Optimizing Convolutional Neural Network Parameters for Better Image Classification

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posted on 07.04.2020 by Manik Dhingra, Sarthak Rawat, Jinan Fiaidhi
The work presented here works on getting higher performances for image recognition task using convolutional neural networks on the MNIST handwritten digits data-set. A range of techniques are compared for improvements with respect to time and accuracy, such as using one-shot Extreme Learning Machines (ELM) in place of the iteratively tuned fully-connected networks for classification, using transfer learning for faster convergence of image classification, and improving the size of data-set and making robust models by image augmentation. The final implementation is hosted on cloud as a web-service for better visualization of the prediction results.

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Email Address of Submitting Author

mdhingra@lakeheadu.ca

Submitting Author's Institution

Lakehead University

Submitting Author's Country

Canada

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