**Euclidean Norm Regularization** is a regularization step used in [generative adversarial networks](https://paperswithcode.com/methods/category/generative-adversarial-networks), and is typically added to both the generator and discriminator losses:

$$ R\_{z} = w\_{r} \cdot ||\Delta{z}||^{2}\_{2} $$

where the scalar weight $w\_{r}$ is a parameter.

Image: [LOGAN](https://paperswithcode.com/method/logan)

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Pose Contrastive Learning

Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment

Euclidean Norm Regularization

Deep Compressed Sensing

PatchMerger is a module for Vision Transformers that decreases the number of tokens/patches passed onto each individual transformer encoder block whilst maintaining performance and reducing compute. PatchMerger takes linearly transforms an input of shape N patches × D dimensions through a learnable weight matrix of shape M output patches × D. This generates M scores, in which a Softmax function is applied for each score. The resulting output has a shape of M × N, which is multiplied to the original input to get an output of shape M × D.

Mathematically, $$Y = \text{softmax}({W^T}{X^T})X$$

Image and formula from: Renggli, C., Pinto, A. S., Houlsby, N., Mustafa, B., Puigcerver, J., & Riquelme, C. (2022). Learning to Merge Tokens in Vision Transformers. arXiv preprint arXiv:2202.12015.

Source	Deep Compressed Sensing
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com