2. Deep Residual Network (ResNet)
One key advantage of deep networks is that they have a great ability to learn different levels of representation from both inputs and feature maps. In classification, segmentation, detection, and a number of other computer vision problems, learning different feature maps generally leads to a better performance.
However, you'll find that it's not easy to train deep networks because the gradient may vanish (or explode) with depth in the shallow layers during backpropagation. Figure 2.2.1 illustrates the problem of vanishing gradient. The network parameters are updated by backpropagation from the output layer to all previous layers. Since backpropagation is based on the chain rule, there is a tendency for the gradient to diminish as it reaches the shallow layers. This is due to the multiplication of small numbers, especially for small loss functions and parameter values.
The number of multiplication operations will be proportional to...
