Advanced GAN techniques
Chapter 1: GAN Foundations Revisited
The Generator-Discriminator Architecture
The Minimax Objective Function
Common Training Instabilities
Limitations of Vanilla GANs
Deep Convolutional GANs (DCGANs) Refresher
Chapter 2: Advanced GAN Architectures
Progressive Growing of GANs (ProGAN)
Style-Based Generator Architecture (StyleGAN)
StyleGAN2 Enhancements
Large Scale GAN Training (BigGAN)
Self-Attention Mechanisms in GANs
Unpaired Image-to-Image Translation (CycleGAN)
Implementing StyleGAN Components: Hands-on Practical
Chapter 3: GAN Training Dynamics and Stabilization
The Challenge of Non-Convergence
Mode Collapse: Causes and Consequences
Alternative Divergences: Wasserstein Distance
Weight Clipping in WGAN
Gradient Penalty (WGAN-GP)
Spectral Normalization
Two Time-Scale Update Rule (TTUR)
Relativistic GANs
Implementing WGAN-GP: Practice
Chapter 4: Conditional and Controllable Generation
Introduction to Conditional GANs (cGANs)
Architectures for cGANs
Information Maximizing GANs (InfoGAN)
StackGAN: Text-to-Image Synthesis
Controlling Attributes via Latent Space Manipulation
Disentanglement Metrics and Challenges
Building a Conditional GAN: Hands-on Practical
Chapter 5: Quantitative and Qualitative Evaluation of GANs
Challenges in Evaluating Generative Models
Qualitative Assessment: Visual Turing Tests
Inception Score (IS): Formulation and Limitations
Fréchet Inception Distance (FID): Formulation
Interpreting FID Scores
Precision and Recall for Distributions
Perceptual Path Length (PPL)
Calculating FID Score: Practice
Chapter 6: GANs Beyond Standard Image Generation
Challenges with Discrete Data: Text Generation
Reinforcement Learning Approaches (SeqGAN, RankGAN)
Continuous Approximations (Gumbel-Softmax)
Audio Synthesis with GANs (WaveGAN, SpecGAN)
Video Generation and Prediction
3D Data Generation (Point Clouds, Meshes)
Graph Generation with GANs
Chapter 7: Implementation, Optimization, and Tooling
Choosing Deep Learning Frameworks
Advanced Optimizers (AdamW, Lookahead)
Hyperparameter Tuning Strategies
Weight Initialization Techniques
Debugging Unstable GAN Training
Mixed Precision Training
Distributed Training Strategies for Large GANs
Profiling and Performance Optimization
Optimizing a GAN Implementation: Practice
Style-Based Generator Architecture (StyleGAN)
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- A Style-Based Generator Architecture for Generative Adversarial Networks, Tero Karras, Samuli Laine, Timo Aila, 2019 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) DOI: 10.48550/arXiv.1812.04948 - The original paper introducing the StyleGAN architecture, detailing the mapping network, AdaIN for style injection, noise addition, and the concept of style mixing for high-resolution image generation with improved disentanglement.
- Analyzing and Improving the Image Quality of StyleGAN, Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, Timo Aila, 2020 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) DOI: 10.48550/arXiv.1912.04958 - This follow-up paper identifies and addresses several issues in the original StyleGAN, leading to StyleGAN2. It introduces architectural changes to remove artifacts and improve perceptual quality through non-saturating path length regularization.
- Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play, David Foster, 2019 (O'Reilly Media) - A comprehensive book that provides practical insights into various generative models, including GANs. It offers a broader understanding of the principles behind StyleGAN and other advanced generative architectures, suitable for advanced learners.