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
Introduction to Diffusion Model Concepts
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- Deep Unsupervised Learning using Nonequilibrium Thermodynamics, Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Stefano Ermon, 2015 Proceedings of the 32nd International Conference on Machine Learning (ICML), Vol. 37 (Proceedings of Machine Learning Research (PMLR)) DOI: 10.5555/3045118.3045353 - Introduces the theoretical framework for diffusion probabilistic models, defining the forward and reverse diffusion processes.
- Denoising Diffusion Probabilistic Models, Jonathan Ho, Ajay Jain, Pieter Abbeel, 2020 Advances in Neural Information Processing Systems (NeurIPS) DOI: 10.48550/arXiv.2006.11239 - This paper made diffusion models practical and achieved state-of-the-art results, introducing the simplified training objective ($L_{simple}$) discussed in the section.
- Generative Modeling by Estimating Gradients of the Data Distribution, Yang Song and Stefano Ermon, 2019 Advances in Neural Information Processing Systems (NeurIPS) DOI: 10.48550/arXiv.1907.05600 - Introduces score-based generative models (Noise-Conditional Score Networks), a parallel and closely related framework to diffusion models, explicitly mentioned as a variant in the text.