Max Pooling is a technique used in artificial neural networks, which enables computers to process data more efficiently. It is used in convolutional neural networks, which are used for image recognition and classification, as well as natural language processing, and other applications.
Max pooling is a form of non-linear down-sampling. It works by sub-sampling the input feature map by dividing it into rectangular pooling regions, and then selecting the maximum value from each of those regions. The pooling regions are selected systematically so that they do not overlap.
Max pooling reduces the dimensionality of the feature map, which reduces the computational cost of the network while still allowing the network to capture relevant information from the input data. Max pooling also provides some degree of robustness against noise or visual distortion in the input data.
In the last few years, max pooling has become a popular tool in deep learning due to its effectiveness and simple implementation. It has been successfully used in various areas including classification tasks and object detection.
Max pooling is an important part of the modern deep learning tool box of computer vision and natural language processing. This technique has been proven to be reliable and efficient in many applications.