What is a convolutional layer?

A convolutional layer is a neural network layer that applies multiple learnable filters to an input to detect local patterns and produce feature maps.

It is one of the core building blocks of a Convolutional Neural Network (CNN).

How it works

Each filter moves across the input and performs a convolution operation on small local regions.

For each position, the filter computes a weighted combination of nearby input values.

The result from one filter forms a feature map.

If a layer contains many filters, it produces many feature maps:

Input → Filter 1 → Feature map 1

Input → Filter 2 → Feature map 2

Input → Filter 3 → Feature map 3

Each filter can learn to respond to a different type of pattern.

What does a convolutional layer learn?

In early CNN layers, filters may learn simple patterns such as:

• Edges
• Corners
• Color transitions
• Simple textures

Deeper convolutional layers can combine these simpler features into more complex patterns.

Important settings

The behavior and output size of a convolutional layer depend on settings such as:

• Kernel or filter size: the size of the local region examined at once.
• Stride: how far the filter moves between positions.
• Padding: whether extra values are added around the input boundary.
• Number of filters: how many output feature maps the layer produces.

These concepts are covered separately in “What is a kernel or filter in a CNN?”, “What is stride in a convolutional neural network?”, and “What is padding in a CNN?”.

For the underlying mathematical operation, see “What is convolution in deep learning?”.

In short, a convolutional layer uses multiple learnable filters to scan local regions of an input and generate feature maps that represent useful patterns.