What is the key insight of ResNet's identity mapping?
mediumAnswer
- A residual block computes y = F(x) + x, where F is a stack of convolutions.
- If the optimal function is close to identity, the block only needs to learn a small correction F(x) ≈ 0 — easier than learning the identity from scratch.
- Enabled training 152-layer nets on ImageNet in 2015.
- Skip connections also let gradients flow directly to earlier layers → no vanishing.
Check yourself — multiple choice
- ResNet removes convolutions
- y = F(x) + x — learn a correction to identity, easier to optimize, gradients flow via skip
- ResNet is shallow
- No skip connections
ResNet: learn residual F(x)=y-x → optimization easier, deep training enabled.
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