In this course, you will :
- Understand GAN components, create basic GANs with PyTorch and advanced DCGANs with convolutional layers, control your GAN, and create conditional GANs.
- Compare generative models, use the FID method to evaluate GAN fidelity and diversity, learn to detect bias in GAN, and put StyleGAN techniques into practise.
- Examine and build Pix2Pix and CycleGAN for image translation, and use GANs for data augmentation and privacy preservation.
- Build Basic Generative Adversarial Networks (GANs)
- Build Better Generative Adversarial Networks (GANs)
- Apply Generative Adversarial Networks (GANs)