One "+x" That Made 100-Layer Networks Trainable: ResNet Skip Connections
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Deep networks have a cruel paradox. In theory, more layers should never hurt — the extra ones could just learn to pass their input through unchanged. In practice, before 2015, stacking more plain layers made networks worse : a 56-layer net had higher training error than a 20-layer one. The gradient vanished on its way back to the early layers, and optimisation couldn't even find that "do nothing" identity mapping. ResNet fixed it with almost absurdly little. The residual reformulation Instead…
1Key Takeaways
- In theory, more layers should never hurt — the extra ones could just learn to pass their input through unchanged.
- In practice, before 2015, stacking more plain layers made networks worse : a 56-layer net had higher training error than a 20-layer one.
- The gradient vanished on its way back to the early layers, and optimisation couldn't even find that "do nothing" identity mapping.
- ResNet fixed it with almost absurdly little.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that in theory, more layers should never hurt — the extra ones could just learn to pass their input through unchanged.
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