Advanced Synthetic Data Generation: GANs and Diffusion Models
Chapter 1: Foundations of Generative Modeling Revisited
Probabilistic Modeling for Generation
Taxonomy of Generative Models
Challenges in High-Dimensional Data Synthesis
GAN Fundamentals
Introduction to Diffusion Model Concepts
Chapter 2: Advanced GAN Architectures and Techniques
Progressive Growing of GANs (ProGAN)
Style-Based Generators (StyleGAN variants)
Unpaired Image-to-Image Translation (CycleGAN)
Conditional GANs: Architectures and Control
Attention Mechanisms in GANs
Analyzing and Manipulating GAN Latent Spaces
Hands-on Practical: Implementing StyleGAN Components
Chapter 3: GAN Training Stability and Optimization
Diagnosing Training Instability: Oscillations and Divergence
Mode Collapse: Causes and Mitigation Strategies
Alternative Loss Functions (WGAN, WGAN-GP, LSGAN)
Regularization Techniques for GANs
Two Time-Scale Update Rule (TTUR)
Hyperparameter Tuning Strategies for GANs
Hands-on Practical: Implementing WGAN-GP
Chapter 4: Diffusion Models: Theory and Advanced Implementation
Mathematical Foundations: Stochastic Differential Equations
Denoising Diffusion Probabilistic Models (DDPM)
Score-Based Generative Modeling
Improved Techniques: DDIM and Variance Schedules
Classifier Guidance and Classifier-Free Guidance
Architectural Considerations for Diffusion Models (U-Net)
Hands-on Practical: Implementing a Basic DDPM
Chapter 5: Evaluating Synthetic Data Quality
Challenges in Generative Model Evaluation
Quantitative Metrics: IS, FID, Precision, Recall
Distributional Metrics: Kernel Inception Distance (KID)
Perceptual Path Length (PPL) for GANs
Qualitative Evaluation Methods
Evaluating Conditional Generation Models
Hands-on Practical: Calculating FID Scores
Chapter 6: Advanced Applications and Integration
High-Resolution Synthesis Strategies
Text-to-Image Synthesis Architectures
Synthetic Data for Augmentation and Privacy
Video Generation with Generative Models
Combining GANs and Diffusion Models
Computational Considerations and Scaling
Hands-on Practical: Conditional Image Generation
Improved Techniques: DDIM and Variance Schedules
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- Denoising Diffusion Implicit Models, Jiaming Song, Chenlin Meng, Stefano Ermon, 2020 ICLR 2021 DOI: 10.48550/arXiv.2010.02502 - Introduces DDIM, a non-Markovian generative process for diffusion models that enables significantly faster and deterministic sampling without retraining.
- Denoising Diffusion Probabilistic Models, Jonathan Ho, Ajay Jain, and Pieter Abbeel, 2020 Advances in Neural Information Processing Systems (NeurIPS) DOI: 10.48550/arXiv.2006.11239 - The foundational paper that introduced Denoising Diffusion Probabilistic Models (DDPMs), defining the forward and reverse processes.
- Improved Denoising Diffusion Probabilistic Models, Alexander Quinn Nichol, Prafulla Dhariwal, 2021 Proceedings of the 38th International Conference on Machine Learning, Vol. 139 (PMLR) DOI: 10.1109/ICCV48922.2021.01050 - Proposes the cosine variance schedule and other improvements to DDPMs, enhancing sample quality and training stability.
- Score-Based Generative Modeling through Stochastic Differential Equations, Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole, 2021 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.2011.13456 - Presents a unified framework for score-based generative models and diffusion models, highlighting the connection between deterministic sampling (like DDIM with η=0) and probability flow ODEs.