Overview
• GAN have two characters contains generator and discriminator.
• Generator generate images from latent code, Discriminator classify images into categories.
• The primary goal of Generator is fool the discriminator, make loss of discriminator maximum.
• In contrast, the main goal of discriminator is correctly classify whether a image(or data) is real(from original dataset) or fake(made by generator).
Dataset
• Dataset: CelebA Face Dataset
• CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations.
• The images in this dataset cover large pose variations and background clutter.
CelebA has large diversities, large quantities, and rich annotations, including
• 10,177 number of identities,
• 202,599 number of face images, and
• 5 landmark locations, 40 binary attributes annotations per image.
• Original Size: 218x178
Your task
• Skeleton Code: https://colab.research.google.com/drive/1mOjpQEfI2ivYtHndNytknfK nclFJ-olE#scrollTo=aGCZQxZSfONu
• Implement a basic DCGAN
• Improve performance of DCGAN
• Use SELU as activation function
• Adopt training process of Relativistic GAN
• More advanced Modifications
• Read notebook to get more details
