Overview of DPN Block
The DPN block is a module that is used in convolutional neural networks (CNN) to enable sharing of common features while still being flexible to explore new features through dual path architectures. It combines the benefits of ResNets and DenseNets.
What is a Dual Path Architecture?
A dual path architecture is a model that has two paths for information to flow through. The first path is a densely connected path that enables exploring new features. The second path is a residual path that enables common features to be reused.
How does a DPN Block work?
The DPN block can be formulated as follows:
xk = โt=1k-1 ftk(ht)
yk = โt=1k-1 vt(ht) = yk-1 + ฯk-1(yk-1)
rk = xk + yk
hk = gk(rk)
Where xk and yk denote the extracted information at k-th step from individual path, vt(ยท) is a feature learning function as ftk. The last equation defines the dual path that integrates them and feeds them to the last transformation function.
The DPN block takes in the input image and passes it through the dual path architecture. The densely connected path explores new features and passes the output to the residual path which in turn enables the common features to be reused. The two paths are then integrated and fed to the last transformation function which gives the final output.
Advantages of DPN Block
The DPN block has several advantages over other CNN architectures. Firstly, it enables sharing of common features while still allowing for exploration of new features through the dual path architecture. This enables a better balance between accuracy and efficiency. Secondly, it has been shown to outperform other architectures such as ResNet and DenseNet on several benchmark datasets. Thirdly, it is relatively straightforward to implement and can be easily integrated into existing CNN architectures.
The DPN block is an image model block used in convolutional neural networks that enables sharing of common features while still allowing for exploration of new features through the dual path architecture. It combines the benefits of ResNets and DenseNets and has several advantages over other CNN architectures. It is relatively easy to implement and can be integrated into existing architectures.
