Please compare max pooling and average pooling in deep learning, and explain in which scenarios you would prefer one over the other.
Answer
Max Pooling: Selects the maximum value within each non-overlapping window (kernel) of the feature map, downsampling while preserving the strongest activation in each region.
Average Pooling: Computes the average of all values within each window of the feature map, downsampling by smoothing and retaining a holistic summary of the region.
The image below shows one example of Max Pooling and Average Pooling.
Max Pooling VS Average Pooling in summary
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