Residual Connection
A Residual Connection (or skip connection) is an architectural feature in deep neural networks that passes the input of a layer directly to its output, bypassing one or more intermediate layers by adding them together.
Frequently Asked Questions
Why do residual connections prevent performance degradation in deep networks?▼
They create an uninterrupted highway for gradients to flow backward during backpropagation, effectively bypassing vanishing gradient bottlenecks in intermediate layers.
What is the mathematical formulation of a residual block?▼
If the layers perform a mapping F(x), the output of the residual block is H(x) = F(x) + x, where x is the original input.
Quick Facts
- CategoryNeural Architectures
- Key ApplicationResNet architectures in computer vision, Transformer layer stacks, and ultra-deep neural networks.
Coverage Trend12 Weeks
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