Generative Adversarial Networks in Practice

Cover
Half Title
Title Page
Copyright Page
Contents
Preface
Acknowledgments
About the Author
1. Introduction
1.1. Preface
1.2. What Is Learning?
1.3. What Is Machine Learning?
1.3.1. Supervised Learning
1.3.2. Unsupervised Learning
1.3.3. Semi-supervised Learning
1.3.4. Supervised vs. Semi-supervised vs. Unsupervised
1.3.5. Reinforcement Learning
1.3.6. Generating Learning
1.3.7. Regression
1.4. What Is Deep Learning?
1.5. Generative Adversarial Networks (GANs)
1.6. About This Book!
1.6.1. Introduction
1.6.2. Data Preprocessing
1.6.3. Model Evaluation
1.6.4. TensorFlow and Keras Fundamentals
1.6.5. Artificial Neural Networks (ANNs)
1.6.6. Deep Neural Networks (DNNs)
1.6.7. Generative Adversarial Networks (GANs)
1.6.8. Deep Convolutional Generative Adversarial Network (DCGAN)
1.6.9. Conditional Generative Adversarial Network (cGAN)
1.6.10. Cycle Generative Adversarial Network (CycleGAN)
1.6.11. Semi-supervised Generative Adversarial Network (SGAN)
1.6.12. Least Squares Generative Adversarial Network (LSGAN)
1.6.13. Wasserstein Generative Adversarial Network (WGAN)
1.6.14. Generative Adversarial Networks (GANs) for Images and Video
1.6.15. Generative Adversarial Networks (GANs) for Voice, Music, and Song
1.7. Terminology
1.7.1. Inputs
1.7.2. Weights
1.7.3. Outputs (Targets)
1.7.4. Activation Function
1.7.5. Vanishing Gradient
1.7.6. Channel
1.7.7. Embedding
1.7.8. Fine-tuning
1.7.9. Data Augmentation
1.7.10. Generalization
1.7.11. Regularization
1.7.12. L1 and L2
1.7.13. Dropout
1.7.14. End-to-end Learning
1.7.15. Error
1.7.16. Training, Testing, and Validation Sets
1.7.17. Overfitting
1.7.18. Underfitting
1.7.19. Confusion Matrix
1.7.20. Error Metrics
1.7.21. Balanced and Unbalanced Datasets
1.7.22. Standardization
1.7.23. Normalization
1.7.24. One-hot Encoding
1.7.25. Generator
1.7.26. Discriminator
1.7.27. Convolutional Layers
1.7.28. Deconvolution
1.7.29. Transposed Convolutional Layers
1.7.30. Deconvolution vs. Transposed Convolutional
1.7.31. Loss Functions
1.7.32. Optimization Functions
1.7.33. Frequency
1.7.34. Amplitude
1.7.35. Timbre
1.7.36. Pitch
1.7.37. Wavelength
1.7.38. Phase
1.7.39. Duration
1.7.40. Intensity
1.7.41. Sample Rate
1.7.42. Score (in Sound)
1.7.43. Performance (in Sound)
1.7.44. Zero-sum Game
1.8. Issues and Challenges in Machine Learning
1.8.1. Bias and Fairness
1.8.2. Overfitting
1.8.3. Data Quality and Quantity
1.8.4. Interpretability
1.8.5. Scalability
1.8.6. Privacy and Security
1.8.7. Hyperparameter Tuning
1.8.8. Transfer Learning
1.8.9. Adversarial Attacks
1.8.10. Ethics and Accountability
1.9. Issues and Challenges in Deep Learning
1.9.1. Overfitting
1.9.2. Underfitting
1.9.3. Vanishing and Exploding Gradients
1.9.4. Computational Complexity
1.9.5. Data Quality
1.9.6. Interpretability
1.9.7. Generalization
1.9.8. Ethical Considerations
1.10. Issues and Challenges in GANs
1.10.1. Mode Collapse
1.10.2. Training Instability
1.10.3. Overfitting
1.10.4. Evaluation
1.10.5. Limited Data
1.10.6. Hyperparameters
1.11. Tips in Machine Learning Implementation
1.11.1. Understand the Problem
1.11.2. Choose the Right Algorithm
1.11.3. Data Preprocessing
1.11.4. Feature Selection
1.11.5. Hyperparameter Tuning
1.11.6. Cross-validation
1.11.7. Regularization
1.11.8. Monitoring and Logging
1.11.9. Interpretability
1.11.10. Reproducibility
1.12. Tips in Deep Learning Implementation
1.12.1. Start with a Small Dataset
1.12.2. Use Pre-trained Models
1.12.3. Use Transfer Learning
1.12.4. Regularize Your Model
1.12.5. Use Early Stopping
1.12.6. Use a Graphics Processing Unit (GPU)
1.12.7. Visualize Your Results
1.12.8. Document Your Experiments
1.12.9. Use Good Coding Practices
1.12.10. Stay Up to Date
1.13. Tips in GAN Implementation
1.13.1. Use a Smaller Learning Rate
1.13.2. Normalize Input Data
1.13.3. Use Batch Normalization
1.13.4. Add Noise to Input Data
1.13.5. Use Different Activation Functions
1.13.6. Use Different Loss Functions
1.13.7. Monitor the Training Process
1.13.8. Use Early Stopping
1.13.9. Generate Samples Periodically
1.13.10. Evaluate the Model Using Multiple Metrics
1.14. Lessons Learned
1.15. Problems
2. Data Preprocessing
2.1. Preface
2.2. Data Preprocessing
2.3. Data Cleaning
2.3.1. Missing Data
2.3.2. Noisy Data
2.3.3. Other Processes
2.4. Data Transformation
2.4.1. Aggregation
2.4.2. Normalizing
2.4.3. Discretization
2.5. Balancing Data
2.5.1. Sampling Techniques
2.5.2. Ensemble Techniques
2.5.3. Combination of Undersampling and Oversampling
2.5.4. Class Weighting
2.5.5. Generating Synthetic Data
2.6. Data Augmentation
2.6.1. Padding
2.6.2. Random Rotating
2.6.3. Rescaling
2.6.4. Vertical and Horizontal Flipping
2.6.5. Translation
2.6.6. Cropping
2.6.7. Zooming
2.6.8. Darkening and Brightening/Color Modification
2.6.9. Grayscaling
2.6.10. Changing Contrast
2.6.11. Adding Noise
2.6.12. Random Erasing
2.7. Data Reduction
2.8. Dataset Partitioning
2.9. Data Preparation Steps
2.9.1. Importing Libraries
2.9.2. Reading Data
2.9.3. Data Cleaning
2.9.4. Data Splitting
2.10. Data Preprocessing Examples
2.10.1. Data-based Example
2.10.2. Project-based Example
2.11. Data Preprocessing Issues
2.11.1. Data Quality
2.11.2. Bias and Fairness
2.11.3. Data Privacy
2.11.4. Scalability
2.11.5. Interpretability
2.12. Data Preprocessing Implementation Tips
2.12.1. Understand Your Data
2.12.2. Choose Appropriate Techniques
2.12.3. Document Changes
2.12.4. Avoid Overfitting
2.12.5. Use Established Libraries
2.12.6. Continuously Monitor Data Quality
2.12.7. Start with Simple Techniques
2.12.8. Test Multiple Techniques
2.12.9. Document Your Steps
2.13. Lessons Learned
2.14. Problems
2.15. Programming Questions
3. Model Evaluation
3.1. Preface
3.2. Hyperparameter Setting
3.2.1. Learning Rate
3.2.2. Batch Size
3.2.3. Number of Epochs
3.2.4. Number of Neurons
3.2.5. Activation Functions
3.3. Optimize the Model
3.3.1. What Is the Noise?
3.3.2. What Is the Bias?
3.3.3. What Is the Variance?
3.4. Bias/Variance
3.5. Identifying Weaknesses in a Model
3.5.1. High Variance
3.5.2. High Bias
3.6. Model Evaluation
3.6.1. Inception Score (IS)
3.6.2. Frechet Inception Distance (FID)
3.6.3. Precision and Recall
3.6.4. Visual Inspection
3.6.5. Human Evaluation
3.6.6. The Hold-out Method
3.6.7. K-fold Cross-validation
3.7. Model Evaluation Issues
3.7.1. Overfitting
3.7.2. Underfitting
3.7.3. Data Bias
3.7.4. Evaluation Metrics
3.7.5. Imbalanced Data
3.7.6. Data Leakage
3.7.7. Hyperparameter Tuning
3.8. Model Evaluation Implementation Tips
3.8.1. Define Evaluation Metrics
3.8.2. Split Data into Train, Validation, and Test Sets
3.8.3. Use Cross-validation
3.8.4. Visualize Performance
3.8.5. Regularization
3.8.6. Early Stopping
3.8.7. Ensemble Models
3.8.8. Fine-tune Hyperparameters
3.9. Lessons Learned
3.10. Problems
3.11. Programming Questions
4. TensorFlow and Keras Fundamentals
4.1. Preface
4.2. How Does TensorFlow Work?
4.3. Tensors
4.4. TensorFlow
4.4.1. Define the Variable
4.4.2. Define the Computation
4.4.3. Operation Execution and Print Results
4.5. Indexing and Slicing
4.5.1. Indexing
4.5.2. Slicing
4.6. Building an NN Using TensorFlow
4.6.1. Import the Data
4.6.2. Loading and Normalizing the Data
4.6.3. Build the Model
4.6.4. Train and Evaluate the Model
4.7. Building a CNN Using TensorFlow
4.7.1. Input Layer
4.7.2. Convolutional and Pooling Layers and Dense Layer
4.7.3. Train the Model
4.7.4. Test the Model
4.8. Keras
4.8.1. Setup and Install
4.8.2. Available Modules
4.8.3. Import Libraries and Modules
4.8.4. Train and Predict the Model
4.8.5. Implement an Example Using Keras
4.9. TensorFlow Issues
4.9.1. Installation Errors
4.9.2. GPU Compatibility Issues
4.9.3. Out-of-memory Error
4.9.4. Slow Training Time
4.9.5. Overfitting
4.9.6. Underfitting
4.9.7. NaN or Infinity Values
4.9.8. Dataset Loading Errors
4.9.9. Debugging Issues
4.9.10. Performance Optimization
4.10. TensorFlow Implementation Tips
4.11. Keras Issues
4.11.1. Keras Installation Errors
4.11.2. Overfitting
4.11.3. Unbalanced Datasets
4.11.4. Gradient Vanishing/Exploding
4.11.5. Memory Issues
4.11.6. Slow Training
4.11.7. Hyperparameter Tuning
4.11.8. Debugging
4.11.9. Compatibility Issues
4.12. Keras Implementation Tips
4.12.1. Use Keras Functional API for Complex Models
4.12.2. Normalize Input Data
4.12.3. Use Early Stopping
4.12.4. Use Regularization
4.12.5. Use Data Augmentation
4.12.6. Monitor the Training Process
4.12.7. Use Transfer Learning
4.12.8. Use GPU Acceleration
4.12.9. Tune Hyperparameters
4.13. Lessons Learned
4.14. Problems
4.15. Programming Questions
5. Artificial Neural Networks Fundamentals and Architectures
5.1. Preface
5.2. Artificial Neural Networks (ANNs)
5.2.1. Biological Neuron
5.2.2. Artificial Neuron
5.3. Linear and Nonlinear Functions
5.3.1. Linear Function
5.3.2. Nonlinear Functions
5.4. ANNs Architectures
5.4.1. Feedforward Neural Networks (FFNNs)
5.4.2. Backpropagation
5.4.3. Single-layer Perceptron
5.4.4. Multi-layer Perceptron (MLP)
5.5. Artificial Neural Networks Issues
5.5.1. Overfitting
5.5.2. Underfitting
5.5.3. Gradient Vanishing/Exploding
5.5.4. Training Time
5.5.5. Data Requirements
5.5.6. Hardware Requirements
5.5.7. Interpretability
5.5.8. Robustness
5.5.9. Bias
5.5.10. Transferability
5.5.11. Architecture Selection
5.5.12. Hyperparameter Tuning
5.5.13. Scalability
5.5.14. Continual Learning
5.5.15. Cost
5.6. Artificial Neural Networks Implementation Tips
5.6.1. Normalize Input Data
5.6.2. Use Appropriate Activation Functions
5.6.3. Implement Early Stopping
5.6.4. Use Regularization
5.6.5. Use Batch Normalization
5.6.6. Implement Learning Rate Decay
5.6.7. Use a Suitable Loss Function
5.6.8. Avoid Using Too Many Layers
5.6.9. Use an Appropriate Optimizer
5.6.10. Use Dropout
5.6.11. Use Data Augmentation
5.6.12. Use Cross-validation
5.6.13. Choose Appropriate Network Architecture
5.6.14. Use Transfer Learning
5.7. Lessons Learned
5.8. Problems
5.9. Programming Questions
6. Deep Neural Networks (DNNs) Fundamentals and Architectures
6.1. Preface
6.2. Deep Neural Networks (DNNs)
6.2.1. What Is Deep Learning?
6.2.2. Deep Learning Needs
6.2.3. How to Deploy Deep Learning More Efficiently?
6.3. Deep Learning Applications
6.3.1. Computer Vision
6.3.2. Natural Language Processing
6.3.3. Robotics
6.3.4. Finance
6.3.5. Biology and Healthcare
6.3.6. Gaming
6.3.7. Agriculture
6.4. Deep Learning Algorithms and Architectures
6.4.1. Recurrent Neural Networks (RNNs)
6.4.2. Long Short-term Memory (LSTM)
6.4.3. Residual Neural Networks (ResNets)
6.4.4. Convolutional Neural Networks (CNNs)
6.4.5. Generative Adversarial Networks (GANs)
6.5. Deep Learning Issues
6.5.1. Vanishing Gradients
6.5.2. Overfitting
6.5.3. Data Preprocessing
6.5.4. Model Architecture
6.5.5. Hardware Limitations
6.5.6. Interpretability
6.5.7. Generalization
6.5.8. Labeling Data
6.5.9. Adversarial Attacks
6.5.10. Ethical Considerations
6.6. Deep Learning Implementation Tips
6.6.1. Choose the Right Framework
6.6.2. Use GPU
6.6.3. Preprocess the Data
6.6.4. Start with a Simple Model
6.6.5. Regularization
6.6.6. Use Transfer Learning
6.6.7. Monitor the Training Process
6.6.8. Hyperparameter Tuning
6.6.9. Save the Model
6.6.10. Use a Reproducible Environment
6.7. Lessons Learned
6.8. Problems
6.9. Programming Questions
7. Generative Adversarial Networks (GANs) Fundamentals and Architectures
7.1. Preface
7.2. Introducing Adversarial Learning
7.2.1. What Is Adversarial Learning?
7.3. GAN Architecture
7.3.1. Generator
7.3.2. Discriminator
7.4. Loss Functions
7.4.1. Minimax Loss
7.4.2. Wasserstein Loss
7.5. GAN Training
7.6. GAN Applications
7.6.1. Image and Video Synthesis
7.6.2. Text-to-Image Synthesis
7.6.3. Style Transfer
7.6.4. Data Augmentation
7.6.5. Medical Image Analysis
7.6.6. 3D Object Generation
7.6.7. Speech Synthesis
7.7. Most Popular GANs
7.7.1. Deep Convolutional GANs (DCGANs)
7.7.2. Conditional GANs (cGANs)
7.7.3. Cycle Generative Adversarial Networks (CycleGAN)
7.7.4. Semi-supervised Generative Adversarial Network (SGAN)
7.7.5. Least Squares Generative Adversarial Network (LSGAN)
7.7.6. Wasserstein Generative Adversarial Networks (WGAN)
7.7.7. Auxiliary Classifier Generative Adversarial Networks (AC-GAN)
7.7.8. Stacked Generative Adversarial Networks (StackGAN)
7.7.9. Progressive, Growing Generative Adversarial Networks (PGAN)
7.7.10. Big Generative Adversarial Networks (BigGAN)
7.7.11. Style-based Generative Adversarial Networks (StyleGAN)
7.8. Develop GAN Models
7.8.1. Analyze the Problem
7.8.2. Select GAN Architecture
7.8.3. Train the Discriminator on Real Dataset
7.8.4. Train Generator
7.8.5. Fake Data on the Discriminator
7.8.6. Train Generator
7.9. Issues in GAN
7.9.1. Mode Collapse
7.9.2. Training Instability
7.9.3. Evaluation of Generated Data
7.9.4. Dataset Bias
7.9.5. Computational Resources
7.9.6. Lack of Interpretability
7.9.7. Data Privacy and Security
7.9.8. Transferability
7.10. Training Approaches and Implementation Tips for GAN
7.10.1. Feature Matching
7.10.2. Normalizing the Inputs, Mini-batch, and Batch Norm
7.10.3. Use Batch Normalization
7.10.4. Use Different Learning Rates for the Generator and Discriminator
7.10.5. Use Different Loss Functions for the Generator and Discriminator
7.10.6. Use a Variety of Evaluation Metrics
7.10.7. Monitor the Gradients
7.10.8. Use Regularization Techniques
7.10.9. Use Appropriate Activation Functions
7.10.10. Use Appropriate Initialization Techniques
7.10.11. Use Early Stopping
7.10.12. Use Transfer Learning
7.10.13. Use Progressive Training
7.10.14. Use Wasserstein Distance
7.10.15. Use Label Smoothing
7.10.16. Use Noise Injections
7.10.17. Use Mini-batch Discrimination
7.11. Lessons Learned
7.12. Problems
7.13. Programming Questions
8. Deep Convolutional Generative Adversarial Networks (DCGANs)
8.1. Preface
8.2. DCGAN Architecture
8.2.1. Generator
8.2.2. Discriminator
8.3. DCGAN Applications
8.3.1. Image Generation
8.3.2. Data Augmentation
8.3.3. Style Transfer
8.3.4. Super-resolution
8.3.5. Anomaly Detection
8.4. DCGAN for CelebA
8.4.1. Import the Libraries
8.4.2. Prepare Data
8.4.3. Discriminator
8.4.4. Generator
8.4.5. Train
8.4.6. Save Generated Images and Train the Model
8.5. DCGAN for MNIST
8.5.1. Import Libraries
8.5.2. Load and Prepare the Dataset
8.5.3. Generator
8.5.4. Discriminator
8.5.5. Discriminator Loss
8.5.6. Generator Loss
8.5.7. Define the Training Loop
8.5.8. Generate and Save Images
8.6. DCGAN Issues
8.6.1. Mode Collapse
8.6.2. Instability during Training
8.6.3. Difficulty in Generating High-resolution Images
8.6.4. Sensitivity to Initialization
8.6.5. Vanishing Gradients
8.6.6. Overfitting
8.6.7. Computational Complexity
8.6.8. Hyperparameter Tuning
8.7. DCGAN Implementation Tips
8.8. Lessons Learned
8.9. Problems
8.10. Programming Questions
9. Conditional Generative Adversarial Network (cGAN)
9.1. Preface
9.2. cGAN Architecture
9.2.1. cGAN Generator
9.2.2. cGAN Discriminator
9.2.3. General Architecture
9.3. cGAN for Fashion Clothing Data
9.3.1. Data Loading
9.3.2. Show the Data Values
9.3.3. Generator
9.3.4. Discriminator
9.3.5. Load Real Sample Data
9.3.6. Select a Real Sample and Generate Data for the Generator and Labels
9.3.7. Generate Fake Sample
9.3.8. Training
9.4. CDCGAN
9.4.1. Importing Libraries
9.4.2. Load Data
9.4.3. Discriminator
9.4.4. Generator
9.4.5. Model
9.4.6. Generate Data
9.4.7. Training
9.5. Pix2Pix GAN for Image-to-Image Translation
9.5.1. Generator
9.5.2. Discriminator
9.5.3. General Model
9.5.4. Loss Function
9.6. cGAN Applications
9.6.1. Image and Video Editing
9.6.2. Data Augmentation
9.6.3. Text-to-image Synthesis
9.6.4. Face Recognition
9.6.5. Fashion and Interior Design
9.6.6. Medical Imaging
9.6.7. Speech Synthesis
9.7. Implementation Pix2Pix
9.7.1. Import Libraries
9.7.2. Load the Dataset
9.7.3. Generator
9.7.4. Generator Loss
9.7.5. Discriminator
9.7.6. Discriminator Loss
9.7.7. Optimizers
9.7.8. Image Generating
9.7.9. Training
9.8. cGAN Issues
9.8.1. Mode Collapse
9.8.2. Lack of Diversity
9.8.3. Difficulty in Training
9.8.4. Sensitivity to Input Noise
9.8.5. Overfitting
9.9. cGAN Implementation Tips
9.9.1. Choose Appropriate Dataset
9.9.2. Use One-hot Encoding
9.9.3. Normalize Inputs
9.9.4. Use Convolutional Layers
9.9.5. Use Batch Normalization
9.9.6. Use Appropriate Loss Functions
9.9.7. Use Appropriate Hyperparameters
9.9.8. Monitor the Training Process
9.9.9. Experiment with Different Architectures
9.9.10. Evaluate the Generated Images
9.10. Lessons Learned
9.11. Problems
9.12. Programming Questions
10. Cycle Generative Adversarial Network (CycleGAN)
10.1. Preface
10.2. CycleGANs
10.3. CycleGANs Applications
10.3.1. Artistic Style Transfer
10.3.2. Image-to-image Translation
10.3.3. Medical Image Analysis
10.3.4. Video Stabilization
10.3.5. Virtual Try-on
10.3.6. Colorization
10.3.7. 3D Shape Synthesis
10.3.8. Domain Adaptation
10.3.9. Style Transfer
10.3.10. Object Transfiguration
10.3.11. Season Transfer
10.3.12. Photograph Generation from Paintings
10.3.13. Photograph Enhancement
10.4. CycleGAN Implementation Using TensorFlow
10.4.1. Import Libraries and Setup
10.4.2. Import and Reuse the Pix2Pix Models
10.4.3. Loss Functions
10.4.4. Checkpoints
10.4.5. Training
10.4.6. Generate Using the Test Dataset
10.5. CycleGAN Issues
10.5.1. Lack of Diversity in Generated Images
10.5.2. Poor Image Quality
10.5.3. Unbalanced Image Domains
10.5.4. Difficulty in Choosing Hyperparameters
10.5.5. Long Training Times
10.5.6. Mode Collapse
10.5.7. Overfitting
10.5.8. Gradient Vanishing/Exploding
10.5.9. Unbalanced Data
10.5.10. Image Artifacts
10.5.11. Lack of Diversity
10.6. CycleGAN Implementation Tips
10.6.1. Preprocessing
10.6.2. Model Architecture
10.6.3. Loss Functions
10.6.4. Hyperparameters
10.6.5. Data Preparation
10.6.6. Training
10.6.7. Evaluation
10.6.8. Deployment
10.7. Lessons Learned
10.8. Problems
10.9. Programming Questions
11. Semi-Supervised Generative Adversarial Network (SGAN)
11.1. Preface
11.2. What Is the Semi-Supervised GAN?
11.3. Semi-Supervised GAN for MNIST
11.3.1. Set Up and Import Libraries
11.3.2. Preprocess the Dataset
11.3.3. Generator
11.3.4. Discriminator
11.3.5. Combined Model
11.3.6. Sample the Dataset
11.3.7. Training
11.3.8. Evaluation and Plotting
11.4. Semi-Supervised Learning GAN Applications
11.4.1. Image Classification
11.4.2. Object Detection
11.4.3. Natural Language Processing
11.4.4. Healthcare
11.4.5. Fraud Detection
11.4.6. Autonomous Vehicles
11.4.7. Recommender Systems
11.4.8. Gaming
11.4.9. Virtual Reality
11.4.10. Advertising
11.5. Semi-Supervised Learning GAN Issues
11.5.1. Lack of Labeled Data
11.5.2. Difficulty in Finding the Right Balance between Labeled and Unlabeled Data
11.5.3. Mode Collapse
11.5.4. Difficulty in Training
11.5.5. Stability Issues
11.5.6. Difficulty in Evaluating Performance
11.6. Semi-Supervised Learning GAN Implementation Tips
11.7. Lessons Learned
11.8. Problems
11.9. Programming Questions
12. Least Squares Generative Adversarial Network (LSGAN)
12.1. Preface
12.2. LSGAN Architecture
12.2.1. Regular GANs
12.2.2. LSGAN
12.2.3. Parameters Selection
12.3. LSGAN Applications
12.3.1. Some Experimental Results
12.3.2. Stability Comparison on the LSUN Dataset
12.3.3. Stability Comparison on Gaussian Mixture Distribution Dataset
12.4. Develop an LSGAN for MNIST
12.4.1. Load Libraries
12.4.2. Loading and Exploring Data
12.4.3. Preprocessing Data
12.4.4. Generator
12.4.5. Discriminator
12.4.6. Compile Discriminator
12.4.7. Combined Network and Model Summary
12.4.8. Training Model
12.5. LSGAN for Caltech_birds2011 Dataset
12.5.1. Import Libraries
12.5.2. Set the Hyperparameters
12.5.3. Load Dataset
12.5.4. Create the Model
12.5.5. Training and Testing
12.6. LSGAN Issues
12.6.1. Mode Collapse
12.6.2. Gradient Vanishing/Exploding
12.6.3. Unstable Training
12.6.4. Hyperparameter Tuning
12.6.5. Evaluation Metrics
12.6.6. Memory Usage
12.6.7. Limited Training Data
12.7. LSGAN Implementation Tips
12.8. Lessons Learned
12.9. Problems
12.10. Programming Questions
13. Wasserstein Generative Adversarial Network (WGAN)
13.1. Preface
13.2. What Is a Wasserstein GAN?
13.2.1. WGAN Algorithm
13.2.2. WGAN-GP Algorithm
13.3. Wasserstein GAN for MNIST
13.3.1. Import Libraries
13.3.2. Import Dataset
13.3.3. Generator
13.3.4. Critic
13.3.5. Loss Functions
13.3.6. Optimizer
13.3.7. Training
13.3.8. Generate Image
13.4. WGAN-GP (WGAN with Gradient Penalty (GP)) on Fashion-MNIST
13.4.1. Import Libraries
13.4.2. Prepare the Data
13.4.3. Discriminator (Critic)
13.4.4. Generator
13.4.5. WGAN-GP Model
13.4.6. Saves Images
13.4.7. Train Model
13.4.8. Generated Image
13.5. Wasserstein GAN Applications
13.5.1. Image Synthesis
13.5.2. Style Transfer
13.5.3. Super-resolution
13.5.4. Data Augmentation
13.5.5. Anomaly Detection
13.5.6. Speech Synthesis
13.5.7. Music Generation
13.5.8. Video Generation
13.6. WGAN’s Issues
13.6.1. Vanishing Gradients
13.6.2. Hyperparameter Tuning
13.6.3. Computational Resources
13.6.4. Evaluation of Results
13.6.5. Gradient Clipping
13.6.6. Mode Collapse
13.6.7. Hyperparameter Tuning
13.7. Wasserstein GAN Implementation Tips
13.8. Lessons Learned
13.9. Problems
13.10. Programming Questions
14. Generative Adversarial Networks (GANs) for Images
14.1. Preface
14.2. Architectures
14.2.1. Direct Methods
14.2.2. Hierarchical Methods
14.2.3. Iterative Methods
14.2.4. Other Methods
14.3. Image Synthesis
14.3.1. Text to Image
14.3.2. Image to Image
14.3.3. MNIST Dataset
14.3.4. CIFAR-10 Dataset
14.3.5. Generating Faces with DCGANs
14.4. Image Restoration Using SRGAN
14.4.1. Import Libraries and Install Prerequisites
14.4.2. Data Preprocessing
14.4.3. Generator and Discriminator
14.4.4. Training
14.4.5. Load and Display
14.5. Image Synthesis Using GAN Issues
14.5.1. Mode Collapse
14.5.2. Lack of Diversity
14.5.3. Training Instability
14.5.4. Limited Applicability to Specific Domains
14.5.5. Lack of Interpretability
14.5.6. Dataset Bias
14.5.7. Computational Complexity
14.5.8. Ethical Concerns
14.6. Implementation Tips for Image Synthesis Using GANs
14.6.1. Choose an Appropriate GAN Architecture for Your Task
14.6.2. Preprocess and Normalize Your Data
14.6.3. Use Data Augmentation Techniques
14.6.4. Regularize Your GAN to Prevent Mode Collapse
14.6.5. Monitor Your GAN Training
14.6.6. Use Transfer Learning to Improve Your GAN
14.6.7. Experiment with Hyperparameters
14.6.8. Consider Using a Pre-trained GAN Model
14.6.9. Evaluate the Quality of Your Generated Images
14.6.10. Use GANs Responsibly
14.7. Lessons Learned
14.8. Problems
14.9. Programming Questions
15. Generative Adversarial Networks (GANs) for Voice, Music, and Song
15.1. Preface
15.2. What Is Sound?
15.2.1. Pitch
15.2.2. Sound Duration
15.2.3. Sound Intensity
15.2.4. Timbre
15.2.5. Preparing audio Data and Feature Extraction
15.3. Audio Synthesis
15.3.1. Music Synthesis
15.3.2. MuseGAN Implementation
15.4. Human Voice Conversion
15.4.1. What Is the Human Voice?
15.4.2. Voice Conversion Approaches
15.4.3. YourTTS Approach
15.4.4. Voice Conversion Using TensorFlow
15.5. Song Conversion
15.5.1. What Is a Song?
15.5.2. Song Generation Using TensorFlow
15.6. Song Conversion Using TensorFlow
15.6.1. Data Collection
15.6.2. Data Preprocessing
15.6.3. Neural Network Architecture
15.6.4. Training the Model
15.6.5. Testing and Evaluation
15.6.6. Code Example
15.7. Issues in GANs for Voice, Music, and Song
15.7.1. Mode Collapse
15.7.2. Training Instability
15.7.3. High-quality Audio Generation
15.7.4. Real-time Generation
15.7.5. Evaluation Metrics
15.7.6. Controllability
15.8. Implementation Tips in GANs for Voice, Music, and Song
15.8.1. Choose the Right Architecture
15.8.2. Use Appropriate Input Representations
15.8.3. Preprocess the Data
15.8.4. Handle Long-range Dependencies
15.8.5. Use Conditional GANs
15.8.6. Monitor Training Progress
15.8.7. Stabilize GAN Training
15.8.8. Experiment with Different Loss Functions
15.8.9. Gradually Increase Model Complexity
15.8.10. Use Domain-specific Knowledge
15.8.11. Evaluate the Results
15.9. Lessons Learned
15.10. Problems
15.11. Programming Questions
Appendix
References
Bibliography
Index