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Showing posts with the label max pooling

How Pooling layer helps in reducing dimension in convolutional neural networks?

One of the most important layer in a CNN architecture is Pooling layer. In this article we will understand what is pooling layer? what does pooling layer do? and how pooling layer works? we will also look at different types of pooling such as max pooling, average pooling and global average pooling. Photo by Andras Kerekes What is Pooling layer and what does pooling layer do?  In simple words Pooling is used for dimensionality reduction in CNN. Why dimensionality reduction? For decreasing the computational power required to process the data. But pooling is not just for reducing the dimension only, it also helps in extracting the dominant features like edges in the image. How pooling layer works? So, now we know that pooling is used for dimensionality reduction but how pooling reduces dimension? Pooling works similar to filters. Consider the below image where we are using a filter of size 2 X 2. In case of filters, we used to multiply filter values to the input element wise and c