Table of Contents
What Is the Core Difference Between Weight Sharing in CNNs and Fully Connected Layers?
Understand the fundamental architectural difference between convolutional and fully connected layers. Learn how convolutional layers use weight sharing (parameter reuse) to efficiently detect spatial features, drastically reducing parameter count compared to fully connected layers where each connection has a unique weight.
Question
Which is a key difference between convolutional and fully connected layers?
A. Convolutional layers cannot be trained with backpropagation.
B. Convolutional layers reuse weights across spatial positions.
C. Fully connected layers reduce parameters by weight sharing.
D. Fully connected layers perform pooling operations.
Answer
B. Convolutional layers reuse weights across spatial positions.
Explanation
CNNs exploit spatial locality with shared filters. This principle, known as parameter sharing or weight sharing, is the defining characteristic that makes Convolutional Neural Networks (CNNs) so efficient and effective for tasks involving spatial data like images.
The primary distinction between a convolutional layer and a fully connected (or dense) layer lies in how they handle parameters (weights).
- Fully Connected Layer: In this type of layer, every neuron in the input is connected to every neuron in the output. Each of these connections has its own unique, learnable weight. For image data, this becomes computationally prohibitive. An input image of just 100×100 pixels, flattened into a vector of 10,000 nodes, connected to a hidden layer of 1,000 neurons would require 10,000 × 1,000 = 10 million weights for that single layer alone. This method also discards all spatial information from the image.
- Convolutional Layer: This layer uses a small filter (e.g., 3×3 or 5×5) that contains a set of weights. This same filter is slid across the entire image, and at each position, the weights are applied to the local patch of pixels it covers. This means the same set of weights (the filter) is reused at every spatial position. The goal is to learn a filter that can detect a specific feature (like a vertical edge or a certain color) regardless of where that feature appears in the image. This parameter sharing drastically reduces the number of weights that need to be learned and preserves the spatial hierarchy of the input.
Analysis of Incorrect Options
A. Convolutional layers cannot be trained with backpropagation: This is false. The weights within the convolutional filters are learnable parameters that are updated and optimized using the backpropagation algorithm, just like the weights in a fully connected layer.
C. Fully connected layers reduce parameters by weight sharing: This is the opposite of how they work. Fully connected layers have the highest parameter count because they do not share weights; every connection is unique. It is the convolutional layers that reduce parameters through weight sharing.
D. Fully connected layers perform pooling operations: This is incorrect. Pooling is a distinct type of layer (e.g., MaxPooling2D) that is typically used between convolutional layers to downsample feature maps. Fully connected layers operate on flattened vectors and do not perform pooling.
Deep Learning with TensorFlow: Build Neural Networks certification exam assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the Deep Learning with TensorFlow: Build Neural Networks exam and earn Deep Learning with TensorFlow: Build Neural Networks certificate.