pooling-layers
Pooling layers are essential components of Convolutional Neural Networks (CNNs) that help reduce the spatial dimensions of feature maps while retaining important information. By downsampling the output from convolutional layers, pooling layers introduce translational invariance, allowing the network to recognize features regardless of their position in the input image. The two most common types of pooling are max pooling, which selects the maximum value from a defined region, and average pooling, which computes the average value. This process not only simplifies the model but also mitigates the risk of overfitting, enhancing the network’s performance in image recognition tasks.
A Gentle Introduction to Pooling Layers for Convolutional Neural Networks
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...
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Introduction To Pooling Layers In CNN
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...
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Forward and Backward propagation of Max Pooling Layer in Convolutional Neural Networks
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...
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Maximum Pooling
<!--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...
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Maximum Pooling
<!--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...
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Maximum Pooling
<!--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...
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Maximum Pooling
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...
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Hikari Connection Pooling —
Connection pooling is a technique for efficiently using and managing the connections of any application. First let’s see the importance of connection pooling —a) Resource efficiency — Opening and clos...
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Pooling Layers For Convolutional Neural Networks (CNN)
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...
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Bilinear pooling for fine-grained visual recognition and multi-modal deep learning
Bilinear pooling originated in the computer vision community as a method for fine-grained visual recognition. Or in less fancy language, a method that looks for specific details when recognizing and…
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Pooling
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...
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Reposting Partial Pooling
Nina Zumel had some good articles on partial pooling estimators that I want to return to. It is a great technique to get more reliable models when using categorical variables. I wrote an introduction ...
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