# Image-to-image translation with GANs
_by Nicolas Beuve (IETR - Vaader) - 2021.02.18_
###### tags: `VAADER` `Reading Group`
## Abstract
<div style="text-align: justify">
Image-to-image translation is a realm aiming at transposing images
from one representation to another, like generating an aerial map of
a region based on a photograph.
Results in this field were greatly improved since the arrival of
GAN models in 2014. GANs (Generative Adversarial Nets) are neural
networks, specialized in sample generation. When applied to an image,
those models are able to generate convincing samples that are similar
to images from a reference dataset while remaining completely original.
</div>

## Slides
{%pdf https://florianlemarchand.github.io/ressources/pdfs/VAADER_Reading_Group/2021-18-02-Beuve-GAN.pdf %}

## Presentation and Discussions

<iframe src="https://videos.insa-rennes.fr/video/0086-vaader-reading-group-3-nicolas-beuve-image-to-image-translation-with-gans/ba6f5e7680162671d90b0a383843913e8a0aa40d74142bc480489fc1ebb662ec/?is_iframe=true" width="640" height="360" style="padding: 0; margin: 0; border:0" allowfullscreen ></iframe>

## Related Material:

* [Generative Adversarial Nets](https://arxiv.org/abs/1406.2661)
* [Conditional Generative Adversarial Nets](https://arxiv.org/abs/1411.1784)
* [Image-to-Image Translation with Conditional Adversarial Networks](https://arxiv.org/abs/1611.07004)
* [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://arxiv.org/abs/1703.10593)