Web Reference: Mar 17, 2026 · Pooling layer is used in CNNs to reduce the spatial dimensions (width and height) of the input feature maps while retaining the most important information. It involves sliding a two-dimensional filter over each channel of a feature map and summarizing the features within the region covered by the filter. Since max pooling is reducing the resolution of the given output of a convolutional layer, the network will be looking at larger areas of the image at a time going forward, which reduces the amount of parameters in the network and consequently reduces computational load. Convolutional Neural Networks (CNNs) | Pooling In this video, we break down CNN Pooling operations—including Max Pooling and Average Pooling—with easy-to-understand examples and...
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Tutorial 24- Max Pooling Layer In CNN
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