---
# System prepended metadata

title: Spatial Transformer Networks
tags: [paper]

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{%hackmd SybccZ6XD %}
###### tags: `paper`

# Spatial Transformer Networks

What is that?
> The spatial transformer module is a **dynamic mechanism** that can actively spatially transform an image

The architecture
> ![](https://i.imgur.com/HpZ0leh.png)
> U: input feature map
> V: output feature

Localisation net
> Predict the parameter of $T_\theta(G)$
> ![](https://i.imgur.com/VfASWTp.png)

The example of $T_\theta(G)$
> ![](https://i.imgur.com/uXzePQG.png)

Why it need the sampler
> The output and input is the same size, so we only need to sample certain part.
> ![](https://i.imgur.com/zZX38ZP.png)

## Appendix

We define an MNIST addition task, where the network must output the sum of the two digits given in the input.
> ![](https://i.imgur.com/PFHIxOs.png)

The classification task
> Find the feature part and predict
> ![](https://i.imgur.com/eDz7Wqs.png)
