global convolutional network

全局卷积网络是一种卷积神经网络结构，它在卷积操作中使用全局信息，而不是局部信息。它可以在处理图像、语音、文本等任务时提供更好的性能。全局卷积网络的主要思想是将卷积核的大小设置为整个输入的大小，这样可以捕捉到全局信息。它可以通过在卷积层中使用全局池化来实现。全局卷积网络在图像分类、目标检测、语音识别等领域都有广泛的应用。

相关问题

Dual graph convolutional network

Dual Graph Convolutional Network (DGCN) is a type of neural network designed to work with graph data. It uses two types of graphs - the primal graph and the dual graph - to represent the input data. The primal graph represents the original data and the dual graph represents the relationships between the original data points.

DGCN works by performing convolution operations on both the primal and dual graphs. This allows the network to capture both local and global relationships between data points. The convolution operation on the primal graph captures local information while the convolution operation on the dual graph captures global information.

DGCN has been used in various applications such as image classification, social network analysis, and recommendation systems. It has shown promising results in these applications and is considered to be a powerful tool for working with graph data.

global average pooling

Global average pooling (GAP) is a pooling operation in deep learning that calculates the average value of each feature map in a convolutional neural network (CNN), resulting in a single value per feature map. It is commonly used as a replacement for fully connected layers in CNNs to reduce the number of parameters and computational complexity of the network.

GAP is applied globally across the spatial dimensions of the feature maps, which means that it reduces the spatial dimensions to a single value. For example, if the feature map has dimensions of 7x7x256, applying GAP will result in a single value for each of the 256 feature maps. This can be useful for classification tasks where the output of the network needs to be a fixed number of values.