stem/AI/Neural Networks/CNN/FCN/ResNet.md

• Residual networks
• 152 layers
• Skips every two layers
  • Residual block
• Later layers learning the identity function
  • Skips help
  • Deep network should be at least as good as shallower one by allowing some layers to do very little
• Vanishing gradient
  • Allows shortcut paths for gradients
• Accuracy saturation
  • Adding more layers to suitably deep network increases training error

Design

• Skips across pairs of Convolutional Layer
  • Elementwise addition
• All layer 3x3 kernel
• Spatial size halves each layer
• Filters doubles each layer
• FCN
  • No fc layer
  • No Max Pooling
    • Except at end
  • No dropout

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