Discover the key differences between fully connected layers and convolutional layers in Convolutional Neural Networks (CNNs). Learn how these layers function, their roles, and why fully connected layers receive input from all neurons in the previous layer.
Table of Contents
Question
How do fully connected layers differ from convolutional layers in a CNN?
A. Fully connected layers receive input from all neurons in the previous layer regardless of spatial location
B. Fully connected layers downsample feature maps produced by convolutional layers
C. Fully connected layers combine and process extracted features to produce the output
D. Fully connected layers apply small filters to extract features at different positions within the image
Answer
A. Fully connected layers receive input from all neurons in the previous layer regardless of spatial location
Explanation
Fully connected (FC) layers and convolutional layers serve distinct purposes in a Convolutional Neural Network (CNN). Here’s a detailed breakdown:
Fully Connected Layers:
- In an FC layer, every neuron is connected to all neurons in the preceding layer. This means that spatial relationships between input features are ignored, and each neuron processes the entire input as a flat vector.
- The FC layer is typically used at the end of a CNN to combine the features extracted by convolutional and pooling layers into a final output (e.g., class probabilities).
- These layers are computationally expensive due to the large number of parameters but are effective for learning complex, non-linear patterns.
Convolutional Layers:
- Convolutional layers are designed to exploit the spatial structure of data, particularly images. Each neuron is only connected to a local region of the input (receptive field), preserving spatial hierarchies.
- They use shared weights (filters) across different regions of the input, which drastically reduces the number of parameters compared to FC layers.
- These layers are efficient and well-suited for tasks like feature extraction, capturing local patterns such as edges and textures.
Why Option A Is Correct:
Fully connected layers process all neurons from the previous layer without considering their spatial arrangement, making them “global” in their connectivity. This characteristic aligns with option A.
Why Other Options Are Incorrect:
B: Fully connected layers do not downsample feature maps; this is typically done by pooling or strided convolutional layers.
C: While FC layers combine features for output generation, this description is incomplete and does not highlight their unique connectivity pattern.
D: Applying small filters to extract features is a defining trait of convolutional layers, not fully connected ones.
In summary, fully connected layers differ from convolutional layers by their dense connectivity pattern, making them crucial for tasks requiring global feature integration.
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