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
Graph Generation with GANs
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- Molecular Generation with Reinforcement Learning and Graph Convolutional Networks, Wengong Jin, Regina Barzilay, and Tommi Jaakkola, 2018 Proceedings of the 35th International Conference on Machine Learning (ICML), Vol. 80 (PMLR (Proceedings of Machine Learning Research)) DOI: 10.5555/3326922.3327092 - Presents a method for generating molecular graphs using reinforcement learning and graph convolutional networks, optimizing for chemical properties.
- NetGAN: Generating Graphs via Random Walks, Aleksandar Bojchevski, Gregor Bachmann, Benjamin Rozemberczki, and Stephan Günnemann, 2018 Proceedings of the 35th International Conference on Machine Learning (ICML), Vol. 80 - Proposes a generative adversarial network model that generates graphs by learning to mimic random walks on target graphs.
- GraphRNN: Generating Realistic Graphs with Deep Learning, Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, and Jure Leskovec, 2018 Proceedings of the 35th International Conference on Machine Learning (ICML), Vol. 80 - Introduces an auto-regressive model for generating realistic graphs node-by-node and edge-by-edge, addressing variable size and complex dependencies.