A **Concatenated Skip Connection** is a type of skip connection that seeks to reuse features by concatenating them to new layers, allowing more information to be retained from previous layers of the network. This contrasts with say, residual connections, where element-wise summation is used instead to incorporate information from previous layers. This type of skip connection is prominently used in DenseNets (and also Inception networks), which the Figure to the right illustrates.

**ClassSR** is a framework to accelerate super-resolution (SR) networks on large images (2K-8K). ClassSR combines classification and SR in a unified framework. In particular, it first uses a Class-Module to classify the sub-images into different classes according to restoration difficulties, then applies an SR-Module to perform SR for different classes. The Class-Module is a conventional classification network, while the SR-Module is a network container that consists of the to-be-accelerated SR network and its simplified versions.

ClassSR

ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic

Concatenated Skip Connection

A **Deep Boltzmann Machine (DBM)** is a three-layer generative model. It is similar to a [Deep Belief Network](https://paperswithcode.com/method/deep-belief-network), but instead allows bidirectional connections in the bottom layers. Its energy function is  as an extension of the energy function of the RBM:

$$ E\left(v, h\right) = -\sum^{i}\_{i}v\_{i}b\_{i} - \sum^{N}\_{n=1}\sum_{k}h\_{n,k}b\_{n,k}-\sum\_{i, k}v\_{i}w\_{ik}h\_{k} - \sum^{N-1}\_{n=1}\sum\_{k,l}h\_{n,k}w\_{n, k, l}h\_{n+1, l}$$

for a DBM with $N$ hidden layers.

Source: [On the Origin of Deep Learning](https://arxiv.org/pdf/1702.07800.pdf)

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com