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Excursion: horses, zebras and a blanket cover

Jürgen Kanz
In this post, I want to show the results of some image transformations with the help of trained neural networks. Of course, this work does not help mankind, but some fun is always good.

I have used Mathematica V12 and in addition two networks from the "Wolfram Neural Net Repository"[1] to run the calculations. Wolfram's "CycleGAN Horse-to-Zebra Translation" net and the "CycleGAN Zebra-to-Horse Translation" net are based on the work of Jun-Yan Zhu [2] for Python software environments.

So, let us see how it works. We start with the image transformation of a horse picture and we want to convert them to zebras.

From Horses to Zebras:

That is nice!

From Horse with a blanket cover to Zebra

The horse looks partly already like a zebra. At the end it is a zebra.

From Horse with two men to Zebra

I do not know these guys, but beside the horse the men become zebras as well.
That is something that should be avoided in a serious transformation.

From Zebra to Horse

That is already something, but the backside of the zebra remains partly as a zebra.

From Zebra to Horse

In this example the transformation works better. I have taken the input image of this example from an official Wolfram notebook.

Finally, it is only one line of code that make this kind of transformation possible in Wolfram Language. That is great. My observations: it is more convincing to transform images of brown horses. With black horses, the results are still good. At least during my tests I have had more transformations that are incomplete with a zebra input. Therefore, I had to put more effort in the selection of a good zebra image as input.

However, it is a fun to make this kind of exercises. In case you want to run the same transformations in your Python / Pytorch environment, please let me know and I will give you the inputs.

[1] Wolfram Neural Net Repository,, last accessed May 11th, 2019
[2] CycleGAN, Jun-Yan Zhu,, last accessed May 11th, 2019

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