Practical Image Generation Using GAN, VAE, and Diffusion Models
About This Course
This comprehensive WSQ course provides in-depth knowledge and hands-on experience in image generation using Generative Adversarial Networks (GAN), Variational Autoencoders (VAE), and Diffusion Models. You'll explore the fundamental concepts and practical applications of GAN frameworks, including DCGAN, WGAN, Conditional GAN, and more. Additionally, the course covers advanced computational modeling techniques, algorithm evaluation, and guidelines for effective GAN implementation across various domains.
Participants will learn to direct GAN modeling efforts, apply computational methodologies, design sophisticated VAE models, and evaluate a broad range of GAN algorithms. You will also gain expertise in applying GAN algorithms to new domains and establishing selection guidelines for their optimal use. This course is ideal for AI professionals, data scientists, and machine learning engineers looking to enhance their skills in state-of-the-art image generation techniques.
What You'll Learn
- direct GAN modeling efforts across the organization
- apply GAN computational methodologies to the problem.
- design advanced computational model with Variational Autoencoders (VAE)
- evaluate a broad range of GAN algorithms
- spear the application of GAN algorithms to new domains
- establish guidelines on GAN algorithm selection
Course Outline:
Topic 1 Introduction to Generative Adversarial Network (GAN)
Overview of Generative Adversarial Network (GAN)
Basic Theory of GAN
General Framework of GAN
Topic 2 Conditional GAN
Overview of Conditional GAN
Basic Application of Conditional GAN
Topic 3 Introduction to Variational Autoencoders (VAEs)
Autoencoders
Variational Autoencoders (VAEs)
Topic 4 Introduction to GAN Algorithms
DC GAN
Disco GAN
Cycle GAN
Star GAN
Energy-Based GAN (EBGAN)
VAE-GAN
Topic 5 Applications of GAN
Photo Editing Using GAN
Style Transfer Using GAN
Image to Image Transformation (Pic2Pic)
Topic 6 GAN Evaluation and Guidelines
Likelihood and Quality of GAN
Objective Evaluation
Mode Dropping
Entry Requirements
Knowledge and Skills
• Able to operate using computer functions
• Minimum Polytechnic Diploma
• Basic programming skill, preferably Python.
Attitude
• Positive Learning Attitude
• Enthusiastic Learner
Experience
• Minimum of 1 year of working experience.