Generative Adversarial Networks (GANs) Training
Introduction to GANs
Learn the fundamentals of Generative Adversarial Networks (GANs). Understand the core concepts, architecture, and applications of GANs in various fields such as image generation, data augmentation, and more.
GAN Architecture and Components
Explore the architecture of GANs, including the Generator and Discriminator networks. Learn about their roles, how they interact, and how they are trained together to produce realistic data.
Training GANs
Study the techniques and challenges involved in training GANs. Learn about loss functions, optimization strategies, and common issues such as mode collapse and non-convergence.
Variations of GANs
Discover different variations of GANs, including Conditional GANs (cGANs), Deep Convolutional GANs (DCGANs), and Wasserstein GANs (WGANs). Understand their specific applications and advantages.
Applications of GANs
Explore various applications of GANs, such as image synthesis, style transfer, data augmentation, and anomaly detection. Learn how GANs are used in real-world scenarios and industry applications.
Advanced GAN Techniques
Learn about advanced techniques and architectures in GAN research, including Progressive Growing GANs, CycleGANs, and StarGANs. Understand how these methods address limitations of traditional GANs.
Implementing GANs with TensorFlow and PyTorch
Gain hands-on experience implementing GANs using popular frameworks like TensorFlow and PyTorch. Follow step-by-step tutorials to build and train GAN models from scratch.
Evaluating GAN Performance
Study methods for evaluating the performance of GANs. Learn about metrics and techniques for assessing the quality and diversity of generated data, including Inception Score (IS) and Fréchet Inception Distance (FID).
Ethical Considerations and Challenges
Explore the ethical implications and challenges associated with GANs, such as the potential for misuse and the impact on privacy and security. Discuss strategies for addressing these issues.
Hands-On Labs and Projects
Engage in hands-on labs and projects to apply your knowledge of GANs. Work on real-world scenarios and projects to develop practical skills in designing, training, and evaluating GAN models.
Generative Adversarial Networks (GANs) Syllabus
Introduction to GANs
- Overview of Generative Models
- Introduction to GANs
- History and Evolution
- Applications of GANs
GAN Architecture
- Generator and Discriminator Networks
- Loss Functions in GANs
- Training Dynamics
- Common Challenges and Solutions
Types of GANs
- Deep Convolutional GANs (DCGANs)
- Conditional GANs (cGANs)
- CycleGANs
- Wasserstein GANs (WGANs)
- Progressive Growing GANs
Implementing GANs
- Setting Up the Environment
- Building a Basic GAN
- Training a GAN on Image Data
- Evaluation Metrics for GANs
Advanced Topics
- Techniques for Stabilizing Training
- Semi-supervised GANs
- GANs for Text and Audio Generation
- StyleGAN and Style Transfer
- Adversarial Attacks and Defense
Advanced GAN Architectures
- Overview of Advanced GAN Variants
- Self-Attention GANs (SAGANs)
- BigGAN and its Innovations
- StyleGAN2 and StyleGAN3
Training Techniques
- Advanced Loss Functions
- Spectral Normalization
- Gradient Penalty and Regularization
- Techniques for Stabilizing GAN Training
Conditional and Multi-modal GANs
- Conditional GANs (cGANs)
- InfoGANs for Representation Learning
- Multi-modal GANs and Applications
Inpainting and Super-Resolution
- GANs for Image Inpainting
- Super-Resolution GANs (SRGANs)
- Face Restoration and Enhancement
Adversarial Training and Robustness
- Adversarial Attacks on GANs
- Defending Against Adversarial Examples
- Robustness and Security in GANs
Unsupervised and Semi-supervised Learning
- Unsupervised Representation Learning
- Semi-supervised Learning with GANs
- Applications in Anomaly Detection
GANs for Sequence Data
- Sequence Generation with GANs
- Text and Audio GANs
- Recurrent GAN Architectures
Training
Basic Level Training
Duration : 1 Month
Advanced Level Training
Duration : 1 Month
Project Level Training
Duration : 1 Month
Total Training Period
Duration : 3 Months
Course Mode :
Available Online / Offline
Course Fees :
Please contact the office for details