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 We recommand to use the notebook (.ipynb) but the Python script (.py) is also provided if more convenient for you.
 
+The purpose of this task is to explore Generative Adversarial Networks (GANs) and their implementation, specifically focusing on an architecture that enables image-to-image translation. 
+
+# Fake numbers generations
+
+ In the first part, we aim to learn and understand the basic concepts of Generative Adversarial Networks through a DCGAN and generate new handwritten numbers from the learned network after showing it real handwritten numbers. 
+
+# Facade generation
+
+In the second part, we implement a cGAN to generate facades from a template image using U-Net architecture.
+
 # How to submit your Work ?
 
-This work must be done individually. The expected output is a repository named gan-cgan on https://gitlab.ec-lyon.fr. It must contain your notebook (or python files) and a README.md file that explains briefly the successive steps of the project. The last commit is due before 11:59 pm on Wednesday, March 29, 2023. Subsequent commits will not be considered.
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+This work must be done individually. The expected output is a repository named gan-cgan on https://gitlab.ec-lyon.fr. It must contain your notebook (or python files) and a README.md file that explains briefly the successive steps of the project. The last commit is due before 11:59 pm on Wednesday, March 29, 2023. Subsequent commits will not be considered.