Skip Connections

Residual Connection

Introduced by He et al. in Deep Residual Learning for Image Recognition

Residual Connections are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions.

Formally, denoting the desired underlying mapping as $\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\mathcal{F}({x}):=\mathcal{H}({x})-{x}$. The original mapping is recast into $\mathcal{F}({x})+{x}$.

The intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.

Source: Deep Residual Learning for Image Recognition


Paper Code Results Date Stars


Task Papers Share
Language Modelling 68 8.70%
Large Language Model 37 4.73%
Semantic Segmentation 32 4.09%
Object Detection 20 2.56%
Retrieval 15 1.92%
Benchmarking 14 1.79%
Prompt Engineering 14 1.79%
Classification 13 1.66%
Autonomous Driving 12 1.53%


Component Type
Batch Normalization
Normalization (optional)
Activation Functions (optional)