**SwiGLU** is an activation function which is a variant of [GLU](https://paperswithcode.com/method/glu). The definition is as follows:

$$ \text{SwiGLU}\left(x, W, V, b, c, \beta\right) = \text{Swish}\_{\beta}\left(xW + b\right) \otimes \left(xV + c\right) $$

**Multi-Level Feature Pyramid Network**, or **MLFPN**, is a feature pyramid block used in object detection models, notably [M2Det](https://paperswithcode.com/method/m2det). We first fuse multi-level features (i.e. multiple layers) extracted by a backbone as a base feature, and then feed it into a block of alternating joint Thinned U-shape Modules ([TUM](https://paperswithcode.com/method/tum)) and Feature Fusion Modules (FFM) to extract more representative, multi-level multi-scale features. Finally, we gather up the feature maps with equivalent scales to construct the final feature pyramid for object detection. Decoder layers that form the final feature pyramid are much deeper than the layers in the backbone, namely, they are more representative. Moreover, each feature map in the final feature pyramid consists of the decoder layers from multiple levels. Hence, the feature pyramid block is called Multi-Level Feature Pyramid Network (MLFPN).

MLFPN

Source	GLU Variants Improve Transformer
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com