About Workshop
This three-day workshop delves into advanced concepts of generative AI, focusing on GANs and Variational Autoencoders (VAEs), stable training techniques, and applications in creative arts, medicine, and bioinformatics. Participants will engage in hands-on sessions and case studies to understand practical challenges and solutions.
Aim
To provide PhD scholars and academicians with advanced skills in generative AI and Generative Adversarial Networks (GANs). This course aims to deepen understanding of GAN architectures, training techniques, and innovative applications, enhancing research and practical implementation capabilities.
What Participants Will Learn
- Master advanced concepts of GANs and VAEs.
- Implement stable GAN training techniques.
- Develop innovative GAN applications in various fields.
- Solve practical challenges in GAN training and optimization.
- Enhance research and practical implementation skills.
Structure
Day 1: Advanced Generative AI Concepts
- Lecture Topics:
- Detailed study of GANs: DCGAN, CycleGAN
- Variational Autoencoders (VAEs) and their applications
- Discussion & Case Studies:
- Case studies in data augmentation and synthetic data generation
- Hands-on session with GAN architectures
- Lecture Topics:
- Techniques for stable GAN training: Wasserstein GAN, Progressive GAN
- Addressing common issues: mode collapse, convergence
- Discussion & Case Studies:
- Practical challenges and solutions
- Interactive session with custom GAN projects
- Lecture Topics:
- Applications in creative arts: AI-generated art, music
- Use cases in medicine and bioinformatics
- Discussion & Case Studies:
- Real-world impact and future trends
- Hands-on session with creative GAN projects
Important Dates
Registration Ends
1:00 pm
Workshop Dates
2024-11-06
5 PM
5 PM
What You Will Gain
Outcomes
- Develop and optimize advanced GAN architectures.
- Implement and fine-tune stable training techniques for GANs.
- Create innovative applications of GANs in creative arts and healthcare.
- Address and solve practical challenges in GAN training.
- Conduct high-level research in generative AI and GANs.
Who Should Attend
Data scientists, AI researchers, computer vision engineers, and academicians in AI and machine learning.
Karar Haider
AI - Engg
Speciality: Creative AI, Bioinformatics, GANs, VAEs, Stable Training