What usually follows convolution layers in a neural network architecture?

In a typical neural network architecture, particularly those used for image processing, convolution layers are often followed by pooling layers. Pooling layers serve the purpose of down-sampling the feature maps generated by convolution layers, effectively reducing their dimensionality while maintaining the most important information. This down-sampling process helps to lessen computational load and control overfitting by providing an abstracted version of the feature map.

Pooling layers often utilize operations like max pooling or average pooling which take the maximum or average value from a group of neighboring pixels, respectively, to create a smaller representation. By reducing the spatial dimensions of the input, pooling layers help the network become more robust to spatial variations and translations in the input data.

The use of pooling layers directly after convolution layers is a common design pattern, leading to increased efficiency in neural networks and encouraging more abstract feature recognition at higher levels of the network. Therefore, the sequence of convolution followed by pooling layers is essential for achieving effective and efficient neural architecture performance.