What are 1x1 convolutions? A 5-minute visual guide.

TL;DR: 1x1 convolutions are channel-wise pooling with trainable parameters.

* They help reduce a model's runtime and memory footprint.
* In-model dimensionality reduction layer.
* Used right before concatenating outputs from different branches of the model.

Here is a visual guide covering key technical details: [What are 1×1 convolutions?](https://open.substack.com/pub/codecompass00/p/1x1-convolutions-supercharge--machine-learning?r=rcorn&utm_campaign=post&utm_medium=web)

https://preview.redd.it/b1c9ivx4e8rc1.png?width=1659&format=png&auto=webp&s=fc9a1dc8004285c121c7dc7c0d17d2e516f97f62

deeplearning dimensionality guide layer memory parameters pooling reduce visual wise