CNN

• CNN = Convolutional Neural Network
• It reduces the number of input nodes
• Tolerate small shifts in images (as pooling is used, small shift result in same weight)
• Take advantage of local context or relation as it uses filter to gather local information
• The matrix obtained as a result convolution operation is called activation map
• Typically ReLU activation function is used
• Usually use Padding in CNN
• Stride in CNN is used to scan through the image
• Typical case:
  • Filter Size of 2 or 3
  • Stride size of 2
  • Max pooling

Steps:

1. Filter scans through left to right, top to bottom
2. Filter weights and Image weights have a dot product (Element-wise multiplication and sum)
3. Use Pooling to gain information

Common Structure of Vision Models

(Filter -> Pooling) x N -> (Dense Network) x M -> Output Layer