Master sophisticated techniques for generating synthetic data using Generative Adversarial Networks (GANs) and Diffusion Models. This course covers advanced architectures, training strategies, evaluation methods, and practical implementation details for creating high-fidelity synthetic datasets.
Learn to implement and optimize state-of-the-art models like StyleGAN, CycleGAN, Denoising Diffusion Probabilistic Models (DDPMs), and score-based generative models. Address challenges such as training stability, mode collapse, and sample quality assessment through rigorous theoretical understanding and hands-on coding.
Prerequisites Strong ML background, Python required
Level:
Advanced GAN Architectures
Implement and train sophisticated GAN models like StyleGAN, ProGAN, and CycleGAN for high-resolution image synthesis and domain adaptation.
Diffusion Model Theory and Implementation
Understand the mathematical foundations of Denoising Diffusion Probabilistic Models (DDPMs) and score-based models, and implement them from scratch.
Training Stability and Optimization
Apply advanced techniques to stabilize GAN and diffusion model training, diagnose issues like mode collapse, and optimize performance.
Conditional Generation Techniques
Develop models capable of generating synthetic data conditioned on specific attributes, labels, or other inputs.
Synthetic Data Evaluation
Utilize advanced quantitative and qualitative metrics to rigorously evaluate the fidelity and diversity of generated synthetic data.
Latent Space Manipulation
Analyze and manipulate the latent spaces of generative models for controlled data synthesis and feature editing.