Impact of Fully Connected Layers on Performance of Convolutional Neural Networks for Image Classification

@article{Basha2020ImpactOF,
  title={Impact of Fully Connected Layers on Performance of Convolutional Neural Networks for Image Classification},
  author={S. H. Shabbeer Basha and S. Dubey and Viswanath Pulabaigari and Snehasis Mukherjee},
  journal={ArXiv},
  year={2020},
  volume={abs/1902.02771}
}
  • S. H. Shabbeer Basha, S. Dubey, +1 author Snehasis Mukherjee
  • Published 2020
  • Computer Science, Engineering, Mathematics
  • ArXiv
  • Abstract The Convolutional Neural Networks (CNNs), in domains like computer vision, mostly reduced the need for handcrafted features due to its ability to learn the problem-specific features from the raw input data. However, the selection of dataset-specific CNN architecture, which mostly performed by either experience or expertise is a time-consuming and error-prone process. To automate the process of learning a CNN architecture, this paper attempts at finding the relationship between Fully… CONTINUE READING
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