Concept
Skip connections are a neural network architecture feature that allows gradients to flow more easily during backpropagation by bypassing one or more layers, thus mitigating the vanishing gradient problem. They enable the construction of very deep networks by directly connecting the output of one layer to the input of a later layer, facilitating improved training and performance in models like ResNet.
Relevant Degrees
3