Max-pooling is a deterministic operation that selects the maximum value from the elements within a fixed-shape pooling window as it slides across an input tensor. Originally introduced in cognitive neuroscience research on hierarchical object recognition, max-pooling has become the dominant pooling method in modern convolutional neural networks. It is generally preferred over average pooling because it confers a degree of invariance to the output—small translations or distortions in the input are less likely to affect the maximum value within a window, making the learned features more robust to spatial shifts.