→ Pooling layers are used in Convolutional Neural Networks (CNNs) to reduce the spatial dimensions (width and height) of the feature maps. This helps in downsampling, reducing computational complexity, and controlling overfitting by summarizing the features (e.g., max pooling or average pooling).
Why the other options are incorrect:
B: Input layers receive raw data and do not perform downsampling.
C: Output layers generate the final prediction.
D: Hidden layers process data but do not specifically perform downsampling unless designed to do so (e.g., convolutional or pooling sublayers).
Official References:
CompTIA DataX (DY0-001) Study Guide – Section 4.3:“Pooling layers are used to downsample feature maps and are critical in CNNs for reducing dimensions.”
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