Architecture
A shortcut that adds a layer's input straight to its output, so the layer only has to learn the change (the residual) instead of a whole new transformation.
A shortcut that adds a layer's input straight to its output, so the layer only has to learn the change (the residual) instead of a whole new transformation. He et al. introduced it in ResNet in 2015 to make very deep networks trainable, and their ImageNet models went up to 152 layers. The Transformer puts a residual connection around every attention and feed-forward sub-layer, and the running sum of those outputs is often called the residual stream.
Primary source: arxiv.org
Example
In practice, developers reach for Residual Connection when they need the capability described above as part of an AI feature or workflow.
Hands-on guides, comparisons, and tutorials that cover Architecture.
FAQ
A shortcut that adds a layer's input straight to its output, so the layer only has to learn the change (the residual) instead of a whole new transformation.
Residual Connection sits in the Architecture part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Developers Digest publishes tutorials and videos that cover Architecture topics including Residual Connection. Check the blog and YouTube channel for hands-on walkthroughs.
Related
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The algorithm that computes how much each weight in a neural network contributed to the error, so gradient descent knows how to adjust it.
A model-parallel technique that splits the weight matrices inside each layer across several GPUs, so every GPU computes a slice of every layer and the partial results are combined with collective communication.

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