Focus of this article
Earlier we learned the basic operations of convolutional neural networks: convolution, stride, padding, and pooling. The above steps are the core operations of convolutional neural networks. Next we will learn a very meaningful concept: receptive field.
what is receptive field
The receptive field in computer vision refers to the range of influence of each neuron in the neural network on the pixels of the input image, that is, the size of the area that the neuron focuses on. In a convolutional neural network, the neurons in each convolutional layer only receive part of the input from the previous layer. This input area is the receptive field of the neuron. The size of the receptive field affects the performance and feature extraction capabilities of the neural network.
In CNN, each layer is composed of multiple convolution kernels. Each convolution kernel scans a small area of the input image and calculates the corresponding features. The size of each convolution kernel determines the size of the receptive field of the neuron. In shallow networks, the receptive field of each neuron usually only covers a small area of the input image, while in deep networks, the receptive field of each neuron can cover most of the input image.
The size of the receptive field has an important impact on the performance of CNN. If the receptive field is too small, the neuron can only see local information of the input image and cannot capture global features, resulting in reduced model performance. If the receptive field is too large, the neurons will be interfered by too much noise and irrelevant information, which will also lead to a decline in model performance. Therefore, it is very important to choose the receptive field size reasonably.
control receptive field
When designing a CNN model, some strategies are usually used to control the size of the receptive field. The most common strategy is to use pooling layer (Pooling Layer) and stride (Stride) and convolution kernel size
The pooling layer can reduce the size of the input image, thereby increasing the receptive field of the neuron.
The step size can control the movement step of the convolution kernel on the input image. The larger the step size, the smaller the receptive field, and more local features can be extracted.
The larger the size of the convolution kernel, the larger the receptive field, and a wider range of features can be extracted. In addition,
In deep neural networks,