CNN Visualizing CNN Feature Maps 5

Question 1

What is the primary goal of applying t-SNE to CNN feature vectors extracted from an intermediate layer?

Accepted Answer

To visualize high-dimensional feature space by projecting similar activations close together in 2D

Answer

t-SNE projects high-dimensional CNN feature vectors into 2D, clustering semantically similar images together, revealing the structure of learned representations.

Question 2

Which approach would best reveal whether a CNN's convolutional filters have learned color-selective detectors?

Accepted Answer

Visualize maximally activating patches from a color image dataset and inspect their dominant hues

Answer

Examining the real image patches that maximally activate each filter exposes their preferred colors, textures, and orientations directly from the data.

Question 3

SHAP (SHapley Additive exPlanations) applied to CNN image classification attributes what to each pixel?

Accepted Answer

A contribution score indicating how much that pixel pushed the prediction toward or away from a class

Answer

SHAP computes each pixel's Shapley value, quantifying its marginal contribution to the final prediction score relative to a reference (background) image.

Question 4

In CNN feature map analysis, what does 'polysemanticity' of a neuron mean?

Accepted Answer

A neuron that responds to multiple unrelated visual concepts

Answer

Polysemantic neurons respond to multiple semantically distinct inputs, making them harder to interpret cleanly — a common finding in large CNN models.

Question 5

When performing activation maximization with total variation (TV) regularization, what does the TV term contribute?

Accepted Answer

It penalizes pixel-to-pixel variation, promoting smoother, more natural-looking visualizations

Answer

Total variation regularization penalizes large differences between neighboring pixels, preventing the gradient-ascent image from becoming a noisy pattern of high-frequency artifacts.

CNN - Convolutional Neural Networks Practice Test

CNN - Convolutional Neural Networks Practice Test

CNN Visualizing CNN Feature Maps 5