What is a skip connection in a neural network?

A skip connection is a shortcut that carries information from an earlier layer directly to a later layer, bypassing one or more intermediate layers.

Normally, information flows through every layer in sequence. A skip connection creates an additional path so later layers can reuse earlier features.

How does it work?

Suppose several layers transform an input x into F(x). A common skip connection combines the original input with that output:

y = F(x) + x

This is an additive skip connection, also called a residual connection. If dimensions differ, a projection can make them compatible.

Another approach is concatenation, where earlier features are joined with newly computed features instead of being added. DenseNet uses this pattern.

Why use skip connections?

• Preserve useful information from earlier layers.
• Create shorter paths for gradients during backpropagation.
• Make very deep networks easier to optimize.

Example: In a residual block, a convolutional path learns F(x) while the shortcut carries x directly to the block's output. The block combines both signals rather than relying only on the convolutional path.

Skip connections are a broader idea; residual connections are one specific type. See “What are residual connections in neural networks?” for that variation.

In short, a skip connection lets information bypass intermediate layers so later layers can access earlier representations directly.