Key Differences Between Fully Connected Layer and Convolutional 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.
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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