Before it
Adding layers past a certain depth made networks worse, not better.
The problem
Gradients degrade through long stacks of transformations.
What it introduced
Add the input of a block to its output, so the block only has to learn a correction.
Why it matters
A vision paper, but every transformer block is wrapped in residual connections. Without them, depth does not train.
What modern models inherit
The skip connection around every attention and feed-forward sublayer.
What came next
Adopted wholesale by the Transformer and everything after it.