VGG network
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The VGG network architecture was introduced by Simonyan and Zisserman in their 2014 paper, .
This network is characterized by its simplicity, using only 3×3 convolutional layers stacked on top of each other in increasing depth. Reducing volume size is handled by max pooling. Two fully-connected layers, each with 4,096 nodes are then followed by a softmax classifier (above).
The “16” and “19” stand for the number of weight layers in the network (columns D and E show in the Figure Below: