**PSFR-GAN** is a semantic-aware style transformation framework for face restoration. Given a pair of LQ face image and its corresponding parsing map, we first generate a multi-scale pyramid of the inputs, and then progressively modulate different scale features from coarse-to-fine in a semantic-aware style transfer way. Compared with previous networks, the proposed PSFR-GAN makes full use of the semantic (parsing maps) and pixel (LQ images) space information from different scales of inputs.

CoLU is an activation function similar to Swish and Mish in properties. It is defined as:
$$f(x)=\frac{x}{1-x^{-(x+e^x)}}$$
It is smooth, continuously differentiable, unbounded above, bounded below, non-saturating, and non-monotonic. Based on experiments done with CoLU with different activation functions, it is observed that CoLU usually performs better than other functions on deeper neural networks.

CoLU

Deeper Learning with CoLU Activation

PSFR-GAN

Progressive Semantic-Aware Style Transformation for Blind Face Restoration

**ShapeConv**, or **Shape-aware Convolutional layer**, is a convolutional layer for processing the depth feature in indoor RGB-D semantic segmentation. The depth feature is firstly decomposed into a shape-component and a base-component, next two learnable weights are introduced to cooperate with them independently, and finally a [convolution](https://paperswithcode.com/method/convolution) is applied on the re-weighted combination of these two components.

Source	Progressive Semantic-Aware Style Transformation for Blind Face Restoration
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com