**DFDNet**, or **DFDNet**, is a deep face dictionary network for face restoration to guide the restoration process of degraded observations. Given a LQ image $I\_{d}$, the DFDNet selects the dictionary features that have the most similar structure with the input. Specially, we re-norm the whole dictionaries via component AdaIN (termed as CAdaIN) based on the input component to eliminate the distribution or style diversity. The selected dictionary features are then utilized to guide the restoration process via dictionary feature transformation.

**CSPDenseNet** is a convolutional neural network and object detection backbone where we apply the Cross Stage Partial Network (CSPNet) approach to [DenseNet](https://paperswithcode.com/method/densenet). The CSPNet partitions the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network.

CSPDenseNet

CSPNet: A New Backbone that can Enhance Learning Capability of CNN

DFDNet

Blind Face Restoration via Deep Multi-scale Component Dictionaries

**ScheduledDropPath** is a modified version of [DropPath](https://paperswithcode.com/method/droppath). In DropPath, each path in the cell is stochastically dropped with some fixed probability during training. In ScheduledDropPath, each path in the cell is dropped out with a probability that is linearly increased over the course of training.

Source	Blind Face Restoration via Deep Multi-scale Component Dictionaries
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com