Commit d26f84b5 authored by Rita-Josy Haddoub's avatar Rita-Josy Haddoub

Update README.md

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......@@ -5,7 +5,8 @@ This is the pretrained model for _Béton_, along with its raw image dataset. Th
![Screen_Shot_2020-06-20_at_12.46.31_PM](/uploads/318bd9915bff47bd174eeb558b6e0a0c/Screen_Shot_2020-06-20_at_12.46.31_PM.png)![IMG_1_copy](/uploads/d025c0252ed28fffb9d672a66552c5b4/IMG_1_copy.png)
# Overview
A collection of photographs showing found Béton have been stored in a dedicated image dataset. With this dataset, I am exploring ways that _‘Machine Learners’_ refers to humans as much as it does to computers. As a single _‘variable’_ which represents experimentation and fragmentation, _Béton_ can be computationally seen within the ‘Latent Space.’In this hidden layer of machine learning, input data is broken down and apart, and tries to re-assemble itself by learning possible compositions. While the results are ambiguous, unsupervised learning does help understand, and predict unknown data better.
A collection of photographs showing found Béton have been stored in a dedicated image dataset. With this dataset, I am exploring ways that _‘Machine Learners’_ refers to humans as much as it does to computers. As a single _‘variable’_ which represents experimentation and fragmentation, _Béton_ can be computationally seen within the ‘Latent Space.’ In this hidden layer of machine learning, input data is broken down and apart, and tries to re-assemble itself by learning possible compositions. While the results are ambiguous, unsupervised learning does help understand, and predict unknown data better.
To visualize the latent space, I feed my image dataset of _Béton_ to a network that de-codes this inner process and generates the re-done _Béton_ back into concrete.
**[Click here](http://igor.gold.ac.uk/~rhadd001/GANtoBeton/) to see a simulated display for an exhibition.**
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