What is the main purpose of pooling in text CNNs (Convolutional Neural Networks)? (Select one option from below.)

Question

BlackTom AI · Accepted Answer

We start by restating the question and listing the answer choices so we can evaluate each option carefully.
Question: What is the main purpose of pooling in text CNNs (Convolutional Neural Networks)? (Select one option from below.)
Answer options:
- 1) Increasing the size of feature maps
- 2) Making the network slower
- 3) Reducing the size of feature maps and highlighting important features
- 4) Removing all features from the data

Now let's analyze each option in turn.

Option 1: Increasing the size of feature maps. This would mean expanding the representation after convolution, which is contrary to the typical function of pooling layers. Pooling is designed to condense information, not amplify spatial dimensions. Therefore, this statement is incorrect because it describes the opposite effect of pooling.

Option 2: Making the network slower. In practice, pooling reduces the spatial resolution of feature maps, which typically decreases the number of computations in subsequent layers and can speed up processing. While there are architectural trade-offs, the primary purpose of pooling is not to slow the network down but to reduce dimensionality and emphasize salient features. Hence this option is incorrect as a description of the main purpose.

Option 3: Reducing the size of feature maps and highlighting important features. This aligns with the core role of pooling: downsampling to decrease spatial dimensions while preserving or emphasizing the most informative or salient signals (e.g., using max pooling to capture strongest activations). This combination—dimensionality reduction plus feature emphasis—is the standard motivation for pooling in CNNs, including text CNN variants that apply convolution over sequences followed by pooling across time steps.

Option 4: Removing all features from the data. This is incorrect because pooling is not intended to erase information completely; it aims to retain essential features while discarding redundant or less informative details. Completely removing features would destroy the signal the network relies on for learning.

In summary, Option 3 correctly captures the principal purpose of pooling in text CNN architectures by both downsampling feature maps and emphasizing the most informative features, whereas the other options describe outcomes that do not reflect the standard role of pooling in these models.

What is the main purpose of pooling in text CNNs (Convolutional Neural Networks)? (Select one option from below.)单项选择题

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