Hands-On Image Processing with Python

ResNet

Simply stacking the layers won't necessarily increase the network depth. They are difficult to train because of the vanishing gradient problem as well. It is an issue wherein the gradient is backpropagated to previous layers and if this happens repeatedly, the gradient may become infinitely small. Hence, as we get deeper, performance gets heavily affected. 

ResNet stands for Residual Network and it introduces shortcuts in the network, which we know by the name of identity shortcut connections. Shortcut connections abide by their name and do the job of skipping one or more layers, hence preventing the stacked layers from degrading performance. The identity layers that are stacked do nothing other than simply stacking identity mappings ...