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
Diagnosing Training Instability: Oscillations and Divergence
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- Generative Adversarial Networks, Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, 2014 arXiv preprint arXiv:1406.2661 DOI: 10.48550/arXiv.1406.2661 - Introduces the foundational GAN framework and the adversarial training objective, which is critical for understanding the source of training instabilities and the min-max game.
- Improved Training of Wasserstein GANs, Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, Aaron Courville, 2017 Advances in Neural Information Processing Systems (NeurIPS) DOI: 10.48550/arXiv.1704.00028 - Presents a significant improvement for GAN stability by introducing the Wasserstein distance with a gradient penalty, directly addressing common issues like vanishing/exploding gradients and mode collapse.
- Are GANs Created Equal? A Critical Review with Initial Guidelines for Fair Comparisons, Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet, 2018 NIPS'18 DOI: 10.48550/arXiv.1711.10337 - Discusses various quantitative and qualitative metrics for evaluating GANs, which are essential for diagnosing training health and recognizing issues like mode collapse through generated samples.
- Deep Learning (Chapter 20: Generative Adversarial Networks), Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016 (MIT Press) - Provides a detailed academic treatment of Generative Adversarial Networks, including their theoretical foundations, training dynamics, and the inherent challenges that lead to instability.