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
Weight Initialization Techniques
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- Understanding the difficulty of training deep feedforward neural networks, Xavier Glorot, Yoshua Bengio, 2010 Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Vol. 9 (JMLR.org) - Introduces Xavier/Glorot initialization, a foundational technique for stabilizing deep neural network training, especially with sigmoid or tanh activations.
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, 2015 Proceedings of the IEEE International Conference on Computer Vision (ICCV) DOI: 10.1109/ICCV.2015.123 - Presents He initialization (also known as Kaiming initialization), specifically designed for networks using ReLU-like activation functions.
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, Alec Radford, Luke Metz, and Soumith Chintala, 2016 International Conference on Learning Representations (ICLR) - A seminal GAN paper that often recommends initializing weights from a zero-centered Normal distribution with a small standard deviation (e.g., 0.02).
- Exact solutions to the nonlinear dynamics of learning in deep linear neural networks, Andrew M. Saxe, James L. McClelland, and Surya Ganguli, 2014 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1312.6120 - Provides theoretical insights into why orthogonal initialization can preserve gradient norms and facilitate learning in deep linear networks, informing its use in non-linear deep models.
- torch.nn.init: Initialization, PyTorch Developers, 2022 (PyTorch) - Official documentation for weight initialization functions in PyTorch, including Kaiming (He), Xavier/Glorot, Normal, and Orthogonal initialization methods.