**Max Pooling** is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map.  It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs.

Image Source: [here](https://computersciencewiki.org/index.php/File:MaxpoolSample2.png)

Karim Hammoudi, Adnane Cabani, Bouthaina Slika, Halim Benhabiles, Fadi Dornaika and Mahmoud Melkemi. SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix Data Augmentation, arXiv:2204.08458, 2022. https://doi.org/10.48550/arxiv.2204.08458

SuperpixelGridMasks

Max Pooling

**PocketNet** is a face recognition model family discovered through [neural architecture search](https://paperswithcode.com/methods/category/neural-architecture-search). The training is based on multi-step knowledge distillation.

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