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Pooling layers are essential components of Convolutional Neural Networks (CNNs) that serve to downsample the feature maps generated by convolutional layers. Their primary purpose is to reduce the spatial dimensions of the feature maps, which helps in minimizing the number of parameters and computations in the network. This downsampling also introduces translation invariance, meaning that the model becomes less sensitive to the exact location of features within the input image, thereby reducing the risk of overfitting ¹².

There are different types of pooling operations, with max pooling and average pooling being the most common. Max pooling selects the maximum value from a defined patch of the feature map, while average pooling computes the average value. Global pooling layers can also be used to downsample the entire feature map into a single value, which is particularly useful in certain architectures ¹⁵.

In summary, pooling layers play a crucial role in enhancing the efficiency and performance of CNNs by consolidating features and reducing the complexity of the model.

Introduction To Pooling Layers In CNN

Towards AI

A Convolutional neural network(CNN) is a special type of Artificial Neural Network that is usually used for image recognition and processing due to its ability to recognize patterns in images. It elim...

A Gentle Introduction to Pooling Layers for Convolutional Neural Networks

MachineLearningMastery.com

Last Updated on July 5, 2019 Convolutional layers in a convolutional neural network summarize the presence of features in an input image. A problem with the output feature maps is that they are sensit...

Convolution layers

Towards Data Science

Convolution layers are fundamental building blocks of computer vision architectures. Neural networks employing convolutions layers are employed in wide-ranging applications in Segmentation…

Layers

Machine Learning Glossary

Layers BatchNorm Convolution Dropout Pooling Fully-connected/Linear RNN GRU LSTM BatchNorm BatchNorm accelerates convergence by reducing internal covariate shift inside each batch. If the individual o...

Pooling Layers For Convolutional Neural Networks (CNN)

Towards Data Science

Background In my previous article, we introduced the key building block behind convolutional neural networks (CNNs), the convolutional layer. Convolutional layers allow the neural network to learn the...

Forward and Backward propagation of Max Pooling Layer in Convolutional Neural Networks

Towards Data Science

Theory and Code Introduction In the last article we saw how to do forward and backward propagation for convolution operations in CNNs. It was found that applying the pooling layer after the convoluti...

Object Pooling in Unity

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Using the new Unity Pooling API to optimize performance In Unity, we know we can create a new instance of a GameObject from a Prefab by calling Instantiate() and destroy it later by calling, surprise...

Maximum Pooling

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Learn more about feature extraction with maximum pooling. Photo by Brock Wegner on Unsplash In the third operation in this series after this part, we will condense with maximum pooling, which in Kera...

Layers of a Convolutional Neural Network

Analytics Vidhya

In the previous chapter of this series, we presented a simple introduction of the Convolutional Neural Network (CNN) which forms the basic building block of most if not all Computer Vision…

Pooling

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In many cases our ultimate task asks some global question about the image, e.g., does it contain a cat? Consequently, the units of our final layer should be sensitive to the entire input. By gradually...

How to implement a Java object pool

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Pooling is not a new technology, it is more like a software design pattern, the main function is to cache a set of initialized objects, ready to use. In most scenarios, the object pool caches object…

Maximum Pooling

Kaggle Learn Courses

<!--TITLE: Maximum Pooling-- Introduction In Lesson 2 we began our discussion of how the base in a convnet performs feature extraction. We learned about how the first two operations in this process oc...