**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.

Unsupervised attributed graph representation learning is challenging since both structural and feature information are required to be represented in the latent space. Existing methods concentrate on learning latent representation via reconstruction tasks, but cannot directly optimize representation and are prone to oversmoothing, thus limiting the applications on downstream tasks. To alleviate these issues, we propose a novel graph embedding framework named Deep Manifold Attributed Graph Embedding (DMAGE). A node-to-node geodesic similarity is proposed to compute the inter-node similarity between the data space and the latent space and then use Bergman divergence as loss function to minimize the difference between them. We then design a new network structure with fewer aggregation to alleviate the oversmoothing problem and incorporate graph structure augmentation to improve the representation's stability. Our proposed DMAGE surpasses state-of-the-art methods by a significant margin on three downstream tasks: unsupervised visualization, node clustering, and link prediction across four popular datasets.

DMAGE

Dynamic SmoothL1 Loss

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

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.

Source	Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com