What is Pooling Layer in CNN – Explain the Types of Pooling

Many times, we struggle to extract features from visual data manually. However, with the help of a Convolutional Neural Network(CNN), you can simplify this process. It is a special type of Artificial Neural Network for image recognition and processing. Since it can recognize patterns in images, it makes your work faster and easier.

Furthermore, CNN has three different layers having various purposes. In this blog, we will focus on the pooling layer in CNN. So read on to get started.

What are Pooling layers?

It is one of the components of Convolutional Neural Networks. They allow convolutional layers to extract features from images. Thus, a Pooling layer consolidates the features identified by CNNs. It can gradually shrink the model’s spatial dimension to reduce the network’s parameters and computations.

The insertion of a pooling layer in CNN is a common practice. This pattern is usual for ordering layers within a convolutional neural network. Moreover, it can be repeated many times in a given model. The pooling layer produces a fresh set of the same number of pooled feature maps. This is because it individually processes each feature map.

Types of Pooling in CNN

There are two common types of poolings in use:

• Max Pooling

It selects the maximum value from every pool. Further, it retains the most prominent traits of the feature map. Now, you will get a better final image than the original image.

• Average Pooling

This pooling is slightly different from max pooling in CNN. This layer works by getting the average of the pool. Then, it retains the average values of traits of the feature map. Further, it smoothes the image but keeps the core of the feature in an image.

What are 3 Layers in CNN?

The CNN structure comprises three key layers:

1. Convolutional Layer

This layer can identify patterns in images. It can also slide a filter over the input image to produce a feature map.

2. Pooling layer

This layer introduces Translation invariance by downsampling the feature map. Thus, it lessens the overfitting of the CNN model.

3. Fully Connected Dense Layer

This layer has the same number of units as classes and output activation functions like “softmax” or “sigmoid,” respectively.

What is the Use of Pooling Layer in CNN?

The filters of Convolutional layers produce a location-dependent feature map. Hence, the feature map keeps the accurate positions of features in the input. Now the pooling layers offer “Translational Invariance” to make the CNN invariant to translations. Thus, even when translating the input of the CNN, the CNN can identify the input features.

A Pooling layer downsampled the output of the Convolutional layers. This layer helps to adjust the filter of a certain size with a certain stride size. Further, it computes the maximum or average of the input,

Advantages of Pooling Layer

The main benefits of these layers in CNN are:

• Dimension Reduction: The main benefit is that they assist in reducing the spatial dimensions of the feature maps. Thus, it lowers the computational cost and avoids overfitting. The pooling layer in CNN reduces the number of parameters in the model.
• Translation Invariance: A Pooling layer helps in translation invariance in the feature maps. Thus, the position of an object in the image will not affect the classification output. This is because it identifies the same features regardless of the object’s position.
• Robustness to Variations: The layer captures the image’s main features and avoids minor variations or noise in the input data. Thus, it adds robustness to the model with changes in scale, rotation, or obstruction.
• Feature selection: These CNN layers also assist in choosing the most essential features from the input. Note that max pooling goes for the most salient features but average pooling secures more information.

Disadvantages of Pooling Layer

Below are some of the drawbacks of the pooling layer in CNN:

• Loss of Information: It is one of the main disadvantages that they often discard a few details from the input feature maps. However, this information can be crucial for the final classification or regression task.
• Over-smoothing: The pooling layer in CNN can over-smoothing the feature maps. Hence, there will be a loss of some delicate details. These details are also crucial for the final classification or regression task.
• Low Feature Resolution: The pooling layer lessens the spatial resolution of the feature maps. Thus, there can be a significant loss. It may cause issues for the network while capturing small or subtle features in the input.
• Hyperparameter tuning: This pooling layer in CNN also inserts hyperparameters like the size of the pooling regions and the stride. Thus, you will have to tune it to get optimal performance. But, it will waste your time and also requires skills in model building.

Key Takeaways

• A Pooling layer is a crucial component of CNN among others.
• It allows dimensionality reduction and introduces translation invariance.
• The pooling layer also assists in feature extraction.
• The maximum pooling process determines the maximum, or largest, value in each patch of each feature map. This is max pooling.
• The average pooling layer works by finding the average of the pool.
• Its various types of layers (Max, Average, and Global pooling) help in image recognition and processing tasks.
• The advantages of a pooling layer include better computational efficiency and less model sensitivity to variations.

Conclusion

In conclusion, the pooling layer in CNN helps detect an object in an image. This layer works regardless of the object’s position in the image. Thus, the result of adding a pooling layer is the reduction of overfitting. It also offers higher efficiency, and faster training times in a CNN model. The max pooling layer finds out the most notable features of an image. But, the average pooling smooths the image keeping the core of its features.

Frequently Asked Questions

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