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.

**Dynamic SmoothL1 Loss (DSL)** is a loss function in object detection where we change the shape of loss function to gradually focus on high quality samples:

$$\text{DSL}\left(x, \beta\_{now}\right) = 0.5|{x}|^{2}/\beta\_{now}, \text{ if } |x| < \beta\_{now}\text{,} $$ 
$$\text{DSL}\left(x, \beta\_{now}\right) = |{x}| - 0.5\beta\_{now}\text{, otherwise} $$ 

DSL will change the value of $\beta\_{now}$ according to the statistics of regression errors which can reflect the localization accuracy. It was introduced as part of the [Dynamic R-CNN](https://paperswithcode.com/method/dynamic-r-cnn) model.

Dynamic SmoothL1 Loss

Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training

CoLU

Deeper Learning with CoLU Activation

**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.

Source	Deeper Learning with CoLU Activation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com