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ResNet MCQ
Identity shortcuts let gradients flow and enable networks with tens or hundreds of layers.
Residual
F(x)+x
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Identity path
Bottleneck
1×1–3×3–1×1
Depth
50–152+ layers
Residual learning
ResNet replaces a stack of layers learning H(x) with residual blocks learning F(x) such that output is F(x)+x when shapes match. Identity shortcuts propagate gradients and ease optimization, enabling much deeper networks on ImageNet and detection backbones.
Why F(x)+x
If the desired mapping is close to identity, learning small perturbations F is easier than learning a full mapping from scratch.
Key ideas
Residual block
Two or three conv layers plus a shortcut summing with the input.
Projection shortcut
1×1 conv on x when channel or spatial sizes change.
Bottleneck
Reduces cost: narrow → 3×3 → narrow channels per block.
Batch norm
Stabilizes training of very deep stacks (used in original ResNet).
Deep stack
stem → stage of residual blocks → global pool → FC / detection head