Prerequisites: Solid understanding of deep learning (backpropagation, optimizers), convolutional neural networks (CNNs), autoencoders, and foundational GAN principles (Generator/Discriminator roles, basic loss functions). Proficiency in Python and a deep learning framework (TensorFlow or PyTorch). Strong mathematical background (calculus, linear algebra, probability theory).

Level: Advanced

What You'll Learn

• Advanced Architectures

  Implement and analyze sophisticated GAN architectures like StyleGAN, BigGAN, and CycleGAN.

• Training Stability

  Apply techniques such as Wasserstein loss, gradient penalties, and spectral normalization to stabilize GAN training.

• Conditional Generation

  Build and train conditional GANs (cGANs) and information-maximizing GANs (InfoGAN) for controlled data synthesis.

• Model Evaluation

  Utilize and interpret advanced metrics like Fréchet Inception Distance (FID) and Inception Score (IS) for GAN performance assessment.

• Theoretical Understanding

  Grasp the mathematical concepts and theoretical justifications behind advanced GAN methods.

• Implementation Proficiency

  Develop the skills to implement, train, and debug complex GAN models using standard deep learning frameworks.