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
Architectures for cGANs
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- Conditional Generative Adversarial Nets, Mehdi Mirza, Simon Osindero, 2014 arXiv preprint arXiv:1411.1784 DOI: 10.48550/arXiv.1411.1784 - Introduces the fundamental concept of Conditional Generative Adversarial Networks, demonstrating how to incorporate class labels into both the generator and discriminator for controlled generation.
- Conditional Image Synthesis with Auxiliary Classifier GANs, Augustus Odena, Christopher Olah, Jonathon Shlens, 2017 International Conference on Machine Learning (ICML) DOI: 10.48550/arXiv.1610.09585 - Proposes the Auxiliary Classifier GAN architecture, where the discriminator predicts both real/fake and the class label, enhancing the generator's ability to produce class-conditional images.
- Large-scale GAN training for high-fidelity natural image synthesis, Andrew Brock, Jeff Donahue, Karen Simonyan, 2019 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1809.11096 - Introduces the Projection Discriminator, significantly improving conditional GAN performance by incorporating an explicit inner product term between image features and class embeddings in the discriminator's output.