Advanced Image Generation with Generative AI

The course is an in-depth exploration of generative artificial intelligence (AI) with a primary focus on image generation. Participants will learn how to create realistic and compelling images using cutting-edge generative AI techniques. The course combines theoretical knowledge with hands-on exercises to provide learners with the necessary skills to implement state-of-the-art image generation models and evaluate the quality of generated images.

In this course, participants will learn how to create realistic and captivating images using cutting-edge generative AI techniques, including Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). They will gain hands-on experience in implementing state-of-the-art image generation models, evaluating image quality, and exploring real-world applications of generative AI in diverse industries.

• Proficiency in Python Programming language
• Fundamentals of Machine Learning
• Deep Learning Basics
• Image Processing Knowledge

Introduction to Generative AI and Image Generation

• Overview of Generative AI and its applications in image generation
• Mathematical and statistical foundations of generative models
• Exploring the image generation process using generative models

Generative Models for Image Generation

• Understanding Variational Autoencoders (VAEs) and their architecture
• Delving into Generative Adversarial Networks (GANs) and their variants
• Comparative analysis of different generative models for image synthesis

Implementing Image Generation Techniques

• Setting up the development environment for generative AI
• Implementing VAEs and GANs using popular deep learning frameworks (e.g., TensorFlow, PyTorch)
• Hands-on exercises: Training and fine-tuning generative models on various datasets

Enhancing Image Realism with Advanced Techniques

• Conditional generation for controlled attribute manipulation
• Style transfer and image synthesis for creative expression
• Case studies: Applying advanced techniques to real-world image generation tasks

 Evaluating Image Quality and Performance

• Quantitative and qualitative evaluation metrics for generated images
• Understanding trade-offs between image quality and model complexity
• Hands-on evaluation exercises: Assessing the fidelity of generative models

Ethical Considerations in Image Generation

• Discussing ethical implications of generative AI in image synthesis
• Addressing bias and fairness concerns in generated images
• Responsible AI usage and its societal impacts

Real-World Applications of Generative AI

• Data augmentation and its role in improving image datasets
• Content creation and art generation using generative models
• Showcasing innovative applications of generative AI in different industries