Compare the similarities, differences and relationships between dilated convolution and atrous convolution

Dilated convolution and dilated convolution are commonly used operations in convolutional neural networks. This article will introduce their differences and relationships in detail.

1. Dilated convolution

Dilated convolution, also known as dilated convolution or dilated convolution, is a convolutional neural network operations in. It is an extension based on the traditional convolution operation and increases the receptive field of the convolution kernel by inserting holes in the convolution kernel. This way, the network can better capture a wider range of features. Dilated convolution is widely used in the field of image processing and can improve the performance of the network without increasing the number of parameters and the amount of calculation. By expanding the receptive field of the convolution kernel, dilated convolution can better process the global information in the image, thereby improving the effect of feature extraction.

The main idea of ​​dilated convolution is to introduce some intervals around the convolution kernel. These intervals allow the convolution kernel to move on the input feature map in a "jumping" manner, thereby Increase the size of the output feature map while keeping the size of the convolution kernel unchanged. Specifically, assuming that the input feature map is \sum_{m}\sum_{n}X_{(i m\times r),(j n\times r)}K_{m,n}

where r is the expansion rate, Represents the size of the hole in the convolution kernel, m and n are the row and column indexes in the convolution kernel. By changing the size of the expansion rate r, feature maps of different receptive fields can be obtained.

2. Atrous convolution

Atrous convolution is a convolution operation commonly used in convolutional neural networks. It is related to expansion The concepts of convolution are very similar, but their implementation is slightly different. The difference between dilated convolution and traditional convolution operation is that some holes are inserted in the convolution operation. These holes can make the convolution kernel "jump" move on the input feature map, thus reducing the size of the output feature map. Increase while keeping the size of the convolution kernel unchanged.

The main idea of ​​dilated convolution is to insert some holes in the convolution kernel. These holes can make the convolution kernel "jump" move on the input feature map, thus making The size of the output feature map is increased while keeping the size of the convolution kernel unchanged. Specifically, assuming that the input feature map is \sum_{m}\sum_{n}X_{(i m\times r),(j n\times r)}K_{m,n}

where r is the void rate, Represents the size of the inserted hole, m and n are the row and column indexes in the convolution kernel. By changing the size of the hole rate r, feature maps of different receptive fields can be obtained.

3. The relationship between dilated convolution and dilated convolution

The concepts of dilated convolution and dilated convolution are very similar. An extension based on the traditional convolution operation. In fact, dilated convolution can be regarded as a special form of dilated convolution, because the hole rate d in dilated convolution is actually the hole rate r-1 in dilated convolution. Therefore, dilated convolution can be regarded as a special kind of dilated convolution, which expands the receptive field of the convolution kernel by inserting holes, and can also be implemented using dilated convolution.

In addition, both dilated convolution and dilated convolution can be used for a variety of tasks in convolutional neural networks, such as image classification, semantic segmentation, etc. They are both effective in different tasks. Can improve the performance of convolutional neural networks. However, since the hole rate d in dilated convolution is discrete, its receptive field is slightly less accurate than that of dilated convolution. Therefore, dilated convolutions may be more commonly used in tasks that require increased receptive fields.

In short, dilated convolution and dilated convolution are commonly used convolution operations in convolutional neural networks. They can be transformed into each other and can also be used in different tasks. Specifically, Which convolution operation needs to be decided based on specific task requirements.

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