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.

A Fully Connected (FC) layer, aka a dense layer, is a type of layer used in artificial neural networks where each neuron or node from the previous layer is connected to each neuron of the current layer. It’s called “fully connected” because of this complete linkage. FC layers are typically found towards the end of neural network architecture and are responsible for producing final output predictions.

Convolutional layers are the building blocks of convolutional neural networks (CNNs), which are primarily used for tasks that require the recognition and processing of spatial data, such as images and videos. These layers apply a convolution operation to the input, passing the result to the next layer.

• Parameter Efficiency: Convolutional layers are more parameter-efficient compared to fully connected layers as convolutional layers can learn local patterns using small filters applied across the input space whereas the fully connected layers learn global patterns which require more parameters.
• Data Suitability: Convolutional layers are specifically advantageous for spatial data such as images, where locality and translation invariance are important whereas the fully connected layers are more flexible and can be used with any form of data.
• Feature Learning: Convolutional layers are designed to automatically learn and generalize features from the input data, such as edges in the initial layers followed by more complex structures in deeper layers and the fully connected layers do not inherently recognize such hierarchical patterns without prior reshaping of the input data.
• Usage in Architectures: In practice, many deep learning architectures use a combination of both types of layers. Convolutional layers are typically used in the earlier stages to extract and learn features, while fully connected layers are often used at the end of the network to make predictions based on these features.

Deep learning has transformed various fields through Fully Connected Neural Networks (FCNNs) and Convolutional Neural Networks (CNNs). FC layers excel in final classification tasks with dense connections, while convolutional layers efficiently extract spatial features with fewer parameters. The combination of these architectures enables powerful image and video processing capabilities.