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by Michele Laurelli

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Convolutional Layer

/ˌkɒnvəˈluːʃənəl ˈleɪər/
Architecture
Definition

A layer in CNNs that applies convolution operations to extract spatial features from input data.

Convolutional layers use learnable filters (kernels) that slide over input to detect local patterns. Multiple filters detect different features like edges, textures, or shapes. Output is a feature map.

Examples

1

Edge detection in images

2

Pattern recognition

3

Feature extraction in CNNs

Related Terms

CNN (Convolutional Neural Network)

A deep learning architecture specialized for processing grid-like data such as images, using convolutional layers.

Feature Map

The output of applying a convolution filter to an input, representing detected features.

Michele Laurelli - AI Research & Engineering