The dense connection strategy is a method used to improve the memory efficiency of Convolutional Neural Networks (CNN). In traditional CNN, the information transfer between independent convolutional layers and fully connected layers is limited, and the idea of parameter sharing only exists inside the convolutional layer. However, as the CNN model deepens, the number of parameters and the amount of calculations will increase rapidly, resulting in a significant increase in the memory footprint and computational complexity of the network. The dense connection strategy emerged as the times require, and by splicing the output of each layer, more information flows, thereby improving the memory efficiency of the network.
In traditional CNN, the output of each layer is only directly connected to its previous layer, while in the dense connection strategy, the output of each layer is connected to the outputs of all previous layers. This dense connection allows each layer to receive information from all previous layers, making the information flow in the network smoother. Compared with traditional CNN that only relies on local receptive field information, the dense connection strategy can make full use of global information, thereby improving the accuracy and robustness of the model.
Another benefit of the dense connection strategy is that it can reduce the number of parameters of the network. In traditional CNN, the parameters of the convolutional layer and the fully connected layer are learned independently, resulting in redundant model parameters. In the dense connection strategy, the output of each layer will be passed to all subsequent layers, so that each layer can share the parameters of the previous layer, thus reducing the number of parameters. This method of parameter sharing can not only reduce the memory footprint of the network, but also help prevent overfitting.
In addition, the dense connection strategy can also improve the gradient propagation efficiency of the network. In traditional CNN, gradients tend to disappear or explode when propagating in the network, making model training difficult. In the dense connection strategy, each layer can receive the gradient information of all previous layers, so that the gradient can be propagated more effectively in the network, thereby accelerating the convergence speed of the model.
In summary, the dense connection strategy is of great significance in improving the memory efficiency of convolutional neural networks. It reduces the number of parameters in the network and improves the efficiency of gradient propagation by increasing the information flow and parameter sharing in the network, thereby improving the performance of the network. This strategy not only performs well in computer vision tasks such as image classification and target detection, but can also be applied to other fields such as natural language processing and recommendation systems. The research and application of dense connection strategies will play an important role in promoting the future development of deep learning.