1. Download the datatest zip file (img_align_celeba.zip) and the attribute file (list_attr_celeba.txt) from http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
2. Create a folder in your Google Drive where upload the downloaded files
3. Open the VAC+GAN notebook and enjoy!
4. If you do not want to download the datatest zip file and the attribute file you can use the VAC+GAN+tfrec notebook. Doing so the dataset is loaded from a Google Cloud bucket. Please notice that if you want to load the datatest from tfrecords after April 2021 you have to use the celeba_tfrec script (celeba_tfrec.py --fn_root PathWhereYourZipFileIsStored --partition_fn PathWhereYouWantToSaveTheTfrecs --number NumberOfTfrecs) to create your own tfrec files and upload them on a Google Cloud bucket that you prefer, then you just have to modify a little bit the notebook code by changing the path of the bucket.

In this kind of approach, the Generative Adversarial Network generator is turned into a conditional generator by placing a multi-class classifier in parallel with the discriminator network and backpropagate the classification error through the generator. This technique is versatile enough to be applied to any GAN.

Images generated from random labels of the dataset

Top-Left: Young = 1, Attractive = 1, Black_Hair = 1, Smiling = 1, Heavy_Makeup = 1, No_Bread = 1

Top-Right: Young = 1, Attractive = 1, Brown_Hair = 1, Smiling = 1, Heavy_Makeup = 1, No_Bread = 1

Bottom-Left: Young = 1, Attractive = 1, Blond_Hair = 1, Smiling = 1, Heavy_Makeup = 1, No_Bread = 1

Bottom-Right: Young = 1, Attractive = 1, Brown_Hair = 1, Smiling = 1, Heavy_Makeup = 1, No_Bread = 1, Bans = 1

Left: Male = 1, Young = 1, Attractive = 1, Black_Hair = 1, Eyeglasses = 1, No_Bread = 1

Right: Male = 1, Attractive = 1, Bald = 1, Big_Nose = 1, No_Bread = 1

Simone Gayed Said - [email protected]
Pierpasquale Colagrande - [email protected]

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