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
Evaluating Conditional Generation Models
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- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium, Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Günter Schmidhuber, 2017 Advances in Neural Information Processing Systems, Vol. 30 DOI: 10.5591/978-1-57766-311-6.196 - Introduces the Fréchet Inception Distance (FID), a widely used metric for evaluating the quality and diversity of samples generated by generative models, often adapted for conditional assessment.
- Learning Transferable Visual Models From Natural Language Supervision, Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever, 2021 Proceedings of the 38th International Conference on Machine Learning (ICML), Vol. 139 - Presents the CLIP model, a foundation for text-to-image and image-to-text tasks, and the basis for the CLIP score used to measure conditional consistency in text-to-image generation.
- Generative Adversarial Networks: A Survey and Taxonomy, Junxian Gui, Zhentao Liu, Jia-Wang Bian, Zi-Yi Dou, Jun-Yan He, Xun Gong, Xiao-Fei Zhang, Wen-Da Qiu, Yong-Wei De, Yi-Hang Shen, Ding-Han Shen, Jian-Jun Li, Zhi-Yan Liu, Li-Fu Zheng, Rui-Shan Liu, De-Wei Kong, Xiao-Jie Jin, Wei-Ming Li, Jing-Hao Zhou, Rui Xu, 2022 ACM Computing Surveys, Vol. 54 DOI: 10.1145/3459676 - A comprehensive survey covering various aspects of Generative Adversarial Networks, including a dedicated section on evaluation metrics that provides a broad context for assessing generative model quality and diversity, relevant to conditional models.
- Perceptual Losses for Real-Time Style Transfer and Super-Resolution, Justin Johnson, Alexandre Alahi, Li Fei-Fei, 2016 European Conference on Computer Vision – ECCV 2016: Amsterdam, The Netherlands, October 8–16, 2016, Proceedings, Part IV (Springer, Cham) DOI: 10.1007/978-3-319-46475-6_43 - Introduces perceptual loss functions, which use features from pre-trained deep neural networks to measure image similarity. This approach is relevant for evaluating conditional generation tasks like style transfer or image-to-image translation where structural and stylistic fidelity are key.