$s \cdot (\mathrm{ReLU}(x))^2 + b$

where $s \in \mathbb{R}$ and $b \in \mathbb{R}$ are shared for all channels and can be set as constants (s=0.8944, b=-0.4472) or learnable parameters.

Multi-head attention consists of multiple attention layers (heads) in parallel with different linear
transformations on the queries, keys, values and outputs. **Multi-query attention** is identical except that the
different heads share a single set of keys and values.

Multi-Query Attention

Fast Transformer Decoding: One Write-Head is All You Need

StarReLU

MetaFormer Baselines for Vision

**Cutout** is an image augmentation and regularization technique that randomly masks out square regions of input during training. and can be used to improve the robustness and overall performance of convolutional neural networks. The main motivation for cutout comes from the problem of object occlusion, which is commonly encountered in many computer vision tasks, such as object recognition,
tracking, or human pose estimation. By generating new images which simulate occluded examples, we not only better prepare the model for encounters with occlusions in the real world, but the model also learns to take more of the image context into consideration when making decisions

Source	MetaFormer Baselines for Vision
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com