What are Pooling Layers in CNN?
In Convolutional Neural Networks (CNNs), the output feature maps from the convolutional layers are downsampled by using pooling layers.
- The basic goal of pooling is to maintain the most relevant information while decreasing the input’s spatial size.
This may avoid overfitting and lower the network’s computational and parameter requirements.
The pooling window size, the stride, and the padding are all examples of hyperparameters that are not trainable in pooling layers. The hyperparameters you choose will be unique to your application and network setup.
Overall, pooling in a convolutional neural network is crucial since it reduces the input space while still keeping relevant information. This has the potential to boost network performance while decreasing computational overhead.
Types of Pooling Layers in CNN
Convolutional neural networks (CNNs) use several pooling layers, such as:
- Max Pooling– It’s the most popular pooling layer because it uses the input feature map’s pooling regions to get the values that are the highest overall. With the help of max pooling, we may minimize the amount of input without losing the most crucial details.
- Global Pooling– Maximum or average value over the full spatial dimension of the input feature map is calculated using global pooling. Global pooling is often used to prepare the data from a convolutional layer to be utilized in a fully connected layer.
- Average Pooling– The average value from each pooling area in the input feature map is used for this operation. When input characteristics are noisy, average pooling may assist in smoothing them out.
- Stochastic Pooling– This method selects a single value from the input feature map’s pooling regions at random. Little translations in the input may be made more forgiving by the use of stochastic pooling.
- Lp Pooling– The Lp norm of each pooling area in the input feature map is used for Lp pooling. In max pooling, the Lp norm is often employed since it generalizes the Euclidean norm. Lp pooling might provide additional wiggle room when downsampling the input feature map.
The application and network topology must be considered while deciding on a pooling layer. While Max pooling is the most used pooling layer, other pooling layers in CNN may be more suited to certain tasks.
Use of Pooling Layer in CNN
In Convolutional Neural Networks (CNNs), pooling layers are crucial because they do two things:
The dimensionality of the feature maps produced by the convolutional layers is reduced by the pooling layers. This helps to limit the network’s computing requirements and forestall any potential for overfitting.
Little translations in the input image are tolerated by pooling layers because of the introduced invariance. This implies that the output of the pooling layer won’t vary much, even if the same item is moved significantly in the input picture.
In addition to these primary uses, the network’s accuracy may be improved by using pooling in CNN to extract more complex information from the input picture. The network is able to learn more generalized features that are less sensitive to changes in the input image’s illumination, orientation, or perspective because of the pooling layers’ downsampling of the feature maps produced by the convolutional layers.
CNNs rely heavily on pooling layers because of their ability to lower the dimensionality of feature maps, make the network more robust to tiny translations, and derive more abstract features from the input picture.
The detection of an item in a picture, independent of its location, is facilitated by pooling layers. By including pooling layers in a CNN model, overfitting is mitigated, efficiency is improved, and the training process is sped up. In contrast to the max pooling layer in CNN, which highlights the most striking aspects of a picture, the average pooling layer softens it while preserving its essential details.