CNN Convolutional Layer Operations 4

Question 1

What is dilated (atrous) convolution, and what advantage does it provide?

Accepted Answer

A convolution with gaps between filter elements, expanding receptive field without adding parameters

Answer

Dilation inserts zeros between filter elements (dilation rate > 1), covering a larger area of the input with the same number of parameters.

Question 2

What is the effect of applying batch normalization after a convolutional layer before the activation function?

Accepted Answer

Normalizes activations to have zero mean and unit variance, stabilizing training

Answer

Batch normalization standardizes pre-activation values across the mini-batch, reducing internal covariate shift and enabling higher learning rates.

Question 3

In a transposed convolution (sometimes called deconvolution), what is the typical use case in CNNs?

Accepted Answer

Spatial upsampling to increase feature map resolution

Answer

Transposed convolutions learn to upsample feature maps and are used in decoder networks, GANs, and semantic segmentation architectures.

Question 4

Given input 28×28×1, filter size 5×5, padding=2, stride=1, how many parameters are in one filter (including bias)?

Accepted Answer

26

Answer

One filter has 5×5×1 = 25 weights plus 1 bias = 26 parameters.

Question 5

Which term describes sharing the same filter weights across all spatial positions in a convolutional layer?

Accepted Answer

Weight sharing (parameter sharing)

Answer

Weight sharing means the same filter is applied at every position, dramatically reducing parameters compared to a fully connected layer.

CNN - Convolutional Neural Networks Practice Test