Application of polling and filling in convolutional neural networks

Convolutional neural network (CNN) is a deep learning neural network that is widely used in image recognition, natural language processing, speech recognition and other fields. The convolutional layer is the most important layer in CNN, and image features can be effectively extracted through convolution operations. In convolutional layers, polling and padding are commonly used techniques that can improve the performance and stability of convolutional layers. Through the polling (pooling) operation, the size of the feature map can be reduced and the complexity of the model can be reduced while retaining important feature information. The padding operation can add extra pixels around the edges of the input image so that the size of the output feature map is the same as the input, avoiding information loss. The application of these technologies is further discussed

1. Polling

Polling is one of the commonly used operations in CNN. By reducing the features Graph size while preserving important features to speed up computation. Usually performed after the convolution operation, it can reduce the spatial dimension of the feature map and reduce the calculation amount and number of parameters of the model. Common polling operations include max pooling and average pooling.

Max pooling is a common operation that obtains the pooling result by selecting the largest feature value within each pooling area. Typically, max pooling uses a pooling area of ​​2x2 and a stride of 2. This operation can retain the most significant features in the feature map, while reducing the size of the feature map and improving the computational efficiency and generalization ability of the model.

Average pooling is a common polling operation, which obtains the pooling result of each pooling area by calculating the average value of the feature values ​​in the area. Average pooling has some advantages over max pooling. First, it can smooth the noise in the feature map and reduce the impact of noise on the final feature representation. Secondly, average pooling can also reduce the size of feature maps, thereby reducing the cost of computing and storage. However, average pooling also has some disadvantages. In some cases, it may lose some important feature information because average pooling averages the feature values ​​across the entire region and may not accurately capture subtle changes in features. Therefore, when designing the convolution god Adds a ring of extra pixels around it, thereby increasing the size of the feature map. The filling operation is usually performed before the convolution operation. It can solve the problem of loss of edge information of the feature map and can also control the output size of the convolution layer.

Padding operations usually include two methods: zero padding and boundary padding.

Zero padding is a common padding method that adds a circle of pixels with zero values ​​around the input feature map. Zero padding can preserve the edge information in the feature map and can also control the output size of the convolutional layer. In convolution operations, zero padding is usually used to ensure that the size of the feature map is the same as the size of the convolution kernel, thereby making the convolution operation more convenient.

Boundary filling is another common filling method, which adds a circle of pixels with boundary values ​​around the input feature map. Boundary filling can preserve the edge information in the feature map and can also control the output size of the convolutional layer. In some special application scenarios, boundary padding may be more suitable than zero padding.

In general, polling and filling are two techniques commonly used in CNN. They can help CNN extract more accurate and useful features and improve the accuracy and generalization ability of the model. . At the same time, these technologies also need to be selected and adjusted according to actual application conditions to achieve optimal results.

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