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Convolutional Neural Networks Lecture


What is a Convolutional Network

  • Convolutional Layers
    • \(3\times3\) or \(5\times5\) (possibly \(7\times7\))
  • Pooling
    • Often maximum; sometimes average or other functions
  • Convolution-Detector-Pooling = Convolutional Unit
    • Convolution
    • Activation (Detector)
    • Pooling

Why is convolution suitable for images?

Features of CNN

  • Capacity (degrees of freedom)
    • fewer weights (DOF) per layer
  • Representation learning
    • may learn edge or corner detection for instance
  • Location invariance
  • Hierarchies; serial and parallel modularisation
  • Fully connected layer at the end.
    • convolutional units learn features
    • last layer uses the features like a traditional ANN with one hidden layer

Considerations in CNN

  • Padding (edge conditions for convolution)
  • Batch Normalisation
    • mini batches
  • Filter/Kernel

Other important features (any ANN)

  • Normalisation
  • Over- and under-training
    • Plot over epochs
  • Feature detection