Dense block意思

"Dense block" is a term used in the context of convolutional neural networks (CNNs), particularly in the architecture of DenseNet, which was introduced in the paper "Densely Connected Convolutional Networks" by G. Huang, Z. Liu, K. Q. Weinberger in 2017.

In a traditional CNN, each layer is connected to the layers before it, but not to the layers after it. This means that the feature maps from earlier layers are not directly passed to later layers. In contrast, a DenseNet architecture aims to address the vanishing gradient problem and encourage feature reuse by connecting each layer to every other layer in the network.

A dense block is a component of DenseNet where each layer is connected to every other layer within the block. This means that the input to each layer is the concatenation of all the feature maps from all the preceding layers in the block. This dense connectivity pattern helps in information flow and feature reuse, leading to better gradient flow and, in some cases, improved performance.

The dense block consists of a series of convolutional layers, and between each pair of consecutive layers, the feature maps are concatenated. This results in an increase in the number of feature maps with each layer, which can lead to a large number of parameters. To mitigate this, DenseNets use "bottleneck" layers and "transition layers" to reduce the number of feature maps between dense blocks.

In summary, a dense block is a building block of DenseNets that facilitates dense connectivity, which is a key feature of DenseNet architecture that distinguishes it from traditional CNNs.