Generative Adversarial Networks (GANs)
Master the Power of GANs: Unleash Creativity and Solve Complex Problems with Generative AI
Skills you will gain:
This program explores the theory and applications of GANs, focusing on their two-component structure (generator and discriminator). Participants will learn how GANs work, delve into advanced variants like DCGAN and CycleGAN, and implement practical projects using GANs for real-world problems such as image synthesis and data generation.
Aim: To equip PhD scholars, researchers, and AI professionals with an in-depth understanding of Generative Adversarial Networks (GANs) and their practical applications. This course covers the fundamentals, advanced techniques, and hands-on experience in building and optimizing GANs for image generation, data augmentation, and creative AI.
Program Objectives:
- Learn the theory behind GANs and how they function.
- Build and train various types of GANs for image generation and creative applications.
- Master advanced GAN techniques like DCGAN, CycleGAN, and Wasserstein GAN.
- Solve real-world problems using GANs, including data augmentation and style transfer.
- Understand and address the challenges of training GANs.
What you will learn?
- Introduction to GANs
- What are GANs?
- Historical Context and Importance of GANs
- Overview of Adversarial Networks (Generator vs. Discriminator)
- GAN Architecture
- Building Blocks of GANs
- Loss Functions for GANs (Minimax Game)
- Training GANs: Key Challenges and Solutions
- Training Dynamics of GANs
- Mode Collapse and Vanishing Gradients
- Techniques to Stabilize GAN Training
- GAN Evaluation Metrics (e.g., Inception Score, FID)
- Deep Dive into Variants of GANs
- Conditional GANs (cGANs)
- Deep Convolutional GANs (DCGANs)
- Wasserstein GANs (WGANs)
- Progressive GANs
- Applications of GANs
- Image Generation
- Text-to-Image Synthesis
- Video and Audio Generation
- Advanced Topics in GANs
- CycleGANs for Image-to-Image Translation
- StyleGAN and Style Transfer
- GANs in Data Augmentation and Privacy Preservation
- Ethical Implications of GANs
- Deepfakes and Their Impact
- Ethical Considerations in Using GANs
- Mitigating Harm in GAN Applications
- GANs in Research and Industry
- Recent Developments in GAN Research
- Applications in Art, Healthcare, and Entertainment
- Deploying GAN Models in Production (Cloud/Edge)
- Hands-on GANs with PyTorch/TensorFlow
- Implementing Basic GANs in PyTorch
- Customizing and Tuning GAN Architectures
- Training GANs on Custom Datasets
Intended For :
AI researchers, data scientists, and professionals with a background in machine learning, neural networks, or computer vision.
Career Supporting Skills