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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

Advanced GAN techniques

Prerequisites Intermediate ML, Python required

Level:

Advanced

Advanced Architectures
Implement and analyze sophisticated GAN architectures like StyleGAN, BigGAN, and CycleGAN.
Training Stability
Apply techniques such as Wasserstein loss, gradient penalties, and spectral normalization to stabilize GAN training.
Conditional Generation
Build and train conditional GANs (cGANs) and information-maximizing GANs (InfoGAN) for controlled data synthesis.
Model Evaluation
Utilize and interpret advanced metrics like Fréchet Inception Distance (FID) and Inception Score (IS) for GAN performance assessment.
Theoretical Understanding
Grasp the mathematical concepts and theoretical justifications behind advanced GAN methods.
Implementation Proficiency
Develop the skills to implement, train, and debug complex GAN models using standard deep learning frameworks.

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