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Deep Learning for Computer Vision, Quiz 2

May 2026 term, 16 Aug 2026, Set 1

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Question 1

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Why do very deep plain CNNs (without skip connections) sometimes show higher training error than shallower CNNs?

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A.
They always overfit because parameter count increases.
B.
Optimization becomes harder; skip connections make learning identity mappings easier and improve gradient flow.
C.
BatchNorm guarantees this never happens.
D.
Using sigmoid activations instead of ReLU always fixes it.

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Why do very deep plain CNNs (without skip connections) sometimes show higher training error than shallower CNNs? MobileNetV1 reduces computation primarily by using: EfficientNet’s key scaling idea is: