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
Regularization Techniques for GANs
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- Spectral Normalization for Generative Adversarial Networks, Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida, 2018 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1802.05957 - Introduces Spectral Normalization to stabilize GAN training by constraining the Lipschitz constant of discriminator layers, making it a standard regularization technique.
- Improved Training of Wasserstein GANs, Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, Aaron Courville, 2017 Advances in Neural Information Processing Systems (NeurIPS), Vol. 30 DOI: 10.48550/arXiv.1704.00028 - Presents the gradient penalty method to enforce the Lipschitz constraint in Wasserstein GANs, significantly enhancing training stability and sample quality.
- Differentiable Augmentation for Data-Efficient GAN Training, Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, Song Han, 2020 Advances in Neural Information Processing Systems (NeurIPS), Vol. 33 DOI: 10.48550/arXiv.2006.10738 - Proposes Differentiable Augmentation, which applies augmentations to both real and fake images during GAN training, serving as a consistency regularization for improved stability and data efficiency.