Aim

This program aims 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.

Final Project

• Students implement a novel GAN application (e.g., image style transfer, data augmentation, etc.)

Participant’s Eligibility

• AI researchers, data scientists, and professionals with a background in machine learning, neural networks, or computer vision.

Program Outcomes

• Ability to build and train advanced GAN models for real-world applications.
• Proficiency in optimizing GANs for stable performance and high-quality outputs.
• Deep understanding of how GANs can be applied in creative industries and data science.
• Skills to address common GAN challenges like mode collapse and training instability.

Program Deliverables

• Access to e-LMS
• Real-Time Project for Dissertation
• Project Guidance
• Paper Publication Opportunity
• Self Assessment
• Final Examination
• e-Certification
• e-Marksheet

Future Career Prospects

• AI Research Scientist
• Deep Learning Engineer
• GAN Specialist
• Data Augmentation Expert
• Computer Vision Engineer
• Creative AI Developer

Job Opportunities

• AI-driven startups
• Creative industries using generative AI
• Research labs focusing on computer vision and generative models
• Data science teams for synthetic data generation
• Gaming and virtual environment companies
• Animation and media production firms