What does a convolutional layer do in a convolutional neural network?

A convolutional layer in a convolutional neural network serves a fundamental role in feature extraction from input data, particularly image data. It does this by applying multiple filters or kernels across the input, detecting various patterns, such as edges, textures, and shapes. Each filter is responsible for capturing specific features, and as the network processes the data through these layers, it builds increasingly abstract representations of the input.

This ability to detect intricate patterns is essential for tasks such as image recognition and classification, where identifying relevant features is crucial for the model's performance. The output from a convolutional layer is typically a set of feature maps, which provide a transformed representation of the input data, encapsulating the features detected by the individual filters.

While other options may imply relevant network functionalities, they do not accurately describe the primary function of a convolutional layer, which is specifically focused on pattern detection through filter application.