Quiz 21: Pooling Layers in CNNs

Evaluate your understanding of pooling operations, feature map visualization, and the role of kernel design in CNNs.

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1. What is the primary purpose of pooling layers in a CNN?

To increase the number of filters

To normalize input images

To reduce spatial dimensions and computational load

To apply activation functions

2. Which pooling technique selects the highest value in a region of the feature map?

Max pooling

Sum pooling

Average pooling

Min pooling

3. What is a typical size for a pooling filter in CNNs?

1x1

2x2

7x7

4x4

4. How does max pooling affect a CNN’s ability to generalize?

It lowers model accuracy

It increases training time significantly

It reduces overfitting by keeping only dominant features

It removes convolution layers

5. Why is visualizing feature maps useful in CNNs?

To understand what the network is learning at each layer

To improve loss functions

To speed up pooling operations

To visualize class probabilities

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