CNN Architecture Fundamentals 2

Question 1

What is the primary purpose of padding in a convolutional layer?

Accepted Answer

To preserve spatial dimensions of the input

Answer

Padding adds border pixels (usually zeros) around the input so the output feature map retains the same spatial size as the input.

Question 2

In a CNN, what does 'stride' control?

Accepted Answer

How many pixels the filter moves between applications

Answer

Stride determines the step size of the filter as it slides across the input, with larger strides producing smaller output feature maps.

Question 3

Which layer type in a CNN is responsible for introducing non-linearity?

Accepted Answer

Activation layer

Answer

Activation layers (e.g., applying ReLU) introduce non-linearity after convolution, enabling the network to learn complex patterns.

Question 4

What distinguishes a depthwise separable convolution from a standard convolution?

Accepted Answer

It applies one filter per input channel, then combines with 1×1 convolutions

Answer

Depthwise separable convolution factorizes a standard convolution into a depthwise step (per-channel) and a pointwise 1×1 step, reducing computation.

Question 5

In AlexNet, what was the purpose of Local Response Normalization (LRN)?

Accepted Answer

To normalize activations across neighboring feature maps for lateral inhibition

Answer

LRN in AlexNet suppresses activations that are weak relative to neighbors across channels, mimicking lateral inhibition seen in biological neurons.

CNN - Convolutional Neural Networks Practice Test