Image Generation

Image generation is a fascinating area of AI that transforms random noise into images. It involves complex models that learn to create realistic images from scratch or modify existing ones. This section covers the mathematical foundations, architectures, and training techniques used in image generation.

📄️ Probabilistic Foundations

Explore the math behind generative AI. Learn how models represent data on a low-dimensional manifold and use VAEs to overcome the intractable partition function.

📄️ Variational Autoencoder

Explore the architecture and training of Variational Autoencoders (VAEs), a powerful class of generative models. Learn how they encode data into a probabilistic latent space and decode it back, enabling the generation of new, coherent samples.