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Optimizing Convolutional Neural Network Parameters for Better Image Classification
  • Manik Dhingra ,
  • Sarthak Rawat ,
  • Jinan Fiaidhi
Manik Dhingra
Lakehead University

Corresponding Author:[email protected]

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Sarthak Rawat
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Jinan Fiaidhi
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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.