Explore a comprehensive analysis of the research paper "DDPM - Diffusion Models Beat GANs on Image Synthesis" in this 55-minute video lecture. Delve into the world of Denoising Diffusion Probabilistic Models (DDPMs) and discover how they outperform GANs in image generation tasks. Learn about the formal probabilistic foundation of DDPMs, their training process, and various improvements such as noise schedule optimization and classifier guidance. Examine the experimental results that demonstrate DDPMs' superior performance on ImageNet datasets at different resolutions. Gain insights into the potential future of image synthesis techniques and the implications for the field of machine learning.
Diffusion Models Beat GANs on Image Synthesis - Machine Learning Research Paper Explained
Overview
Syllabus
- Intro & Overview
- Denoising Diffusion Probabilistic Models
- Formal derivation of the training loss
- Training in practice
- Learning the covariance
- Improving the noise schedule
- Reducing the loss gradient noise
- Classifier guidance
- Experimental Results
Taught by
Yannic Kilcher
Related Courses
-
Diffusion Models Beat GANs on Image Synthesis - ML Coding Series - Part 2
-
Stable Diffusion and Friends - High-Resolution Image Synthesis via Two-Stage Generative Models
-
OpenAI GLIDE - Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
-
Generative Adversarial Networks and Stable Diffusion
-
VQ-GAN - Taming Transformers for High-Resolution Image Synthesis - Paper Explained
-
TransGAN - Two Transformers Can Make One Strong GAN - Machine Learning Research Paper Explained
