convolutional neural network (CNN)

A convolutional neural network (CNN) is a neural network architecture that employs local receptive fields and weight-sharing filters to process structured data, such as images, producing feature maps that are (approximately) translation equivariant.

After early work like LeNet in handwriting recognition and the breakthrough AlexNet at the 2012 ImageNet challenge, CNNs became the dominant approach in vision. Variants now extend to audio, video, and other grid-structured domains.

Compared to fully connected networks, their local structure and parameter sharing improve sample efficiency, computational costs, and generalization on grid-like inputs.

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