CNN Visualizing CNN Feature Maps 2

Question 1

What does a feature map (activation map) represent in a CNN layer?

Accepted Answer

The output of applying a learned filter across the input

Answer

A feature map is produced by convolving a learned filter over the input, showing where and how strongly that filter's pattern is detected.

Question 2

When visualizing early-layer feature maps, what kind of patterns are typically detected?

Accepted Answer

Low-level patterns like edges, colors, and textures

Answer

Early CNN layers respond to simple, low-level visual features such as oriented edges, color gradients, and basic textures.

Question 3

Which Python library function is commonly used to extract intermediate layer outputs for feature map visualization in Keras?

Accepted Answer

Model(inputs, intermediate_layer.output)

Answer

Creating a new Keras Model with the original input and an intermediate layer's output allows you to forward-pass an image and retrieve that layer's activations.

Question 4

If a feature map appears mostly dark (near-zero activations), what does this indicate?

Accepted Answer

The filter's learned pattern is not present in the input region

Answer

Near-zero activations mean the convolutional filter did not detect its corresponding pattern in those spatial locations of the input.

Question 5

What is the spatial dimension relationship between an input feature map and output feature map when using a 3×3 filter with padding='same' and stride=1?

Accepted Answer

Output is the same spatial size as the input

Answer

With 'same' padding and stride=1, zero-padding is added so the output spatial dimensions exactly match the input dimensions.

CNN - Convolutional Neural Networks Practice Test

CNN - Convolutional Neural Networks Practice Test

CNN Visualizing CNN Feature Maps 2