Fully Connected Layer vs Convolutional Layer
Features |
Fully Connected Layer |
Convolutional Layer |
---|---|---|
Definition |
Every neuron is connected to every neuron in the previous layer. |
Neurons are connected only to a local region of the previous layer. |
Connectivity |
Dense connections; each neuron connects to all neurons in the previous layer. |
Sparse connections; each neuron connects only to a local patch of the input. |
Parameters |
Large number of parameters due to full connectivity. |
Fewer parameters due to shared weights and local connectivity. |
Weight Sharing |
No weight sharing; each connection has its own weight. |
Weights are shared across spatial positions, reducing the number of parameters. |
Typical Use Cases |
Final classification layers in neural networks. |
Feature extraction, especially in image and video processing. |
Computation Cost |
Higher computational cost due to large number of connections. |
Lower computational cost per neuron due to local connections. |
Overfitting |
Higher risk of overfitting due to large number of parameters. |
Lower risk of overfitting due to fewer parameters and regularization effects of local connections. |
Dimensionality Reduction |
Does not inherently reduce dimensionality. |
Can reduce dimensionality through pooling layers. |
Examples |
Multilayer Perceptron (MLP), Dense layers in CNNs. |
Convolutional Neural Networks (CNNs), such as layers in AlexNet, VGGNet. |
Fully Connected Layer vs Convolutional Layer
Confusion between Fully Connected Layers (FC) and Convolutional Layers is common due to terminology overlap. In CNNs, convolutional layers are used for feature extraction followed by FC layers for classification that makes it difficult for beginners to distinguish there roles.
This article compares Fully Connected Layers (FC) and Convolutional Layers (Conv) in neural networks, detailing their structures, functionalities, key features, and usage in deep learning architectures.
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