A Diffusion Model Tutorial Worth Your Time, from Purdue University

Diffusion can not only imitate better, but also "create".

Diffusion Model is an image generation model. Compared with the well-known algorithms such as GAN and VAE in the field of AI, the diffusion model takes a different approach. Its main idea is a process of first adding noise to the image and then gradually denoising it. How to denoise and restore the original image is the core part of the algorithm. The final algorithm is able to generate an image from a random noisy image.

In recent years, the phenomenal growth of generative AI has enabled many exciting applications in converting text into image generation, video generation, and more. The basic principle behind these generative tools is the concept of diffusion, a special sampling mechanism that overcomes some of the shortcomings of previous methods that were considered difficult to solve.

Recently, Stanley H. Chan from Purdue University released a tutorial on diffusion models "Tutorial on Diffusion Models for Imaging and Vision", which provides an intuitive and detailed explanation of the technology in this direction.

The goal of this tutorial is to discuss the basic ideas of diffusion models. The target audience includes scientists and graduate students interested in diffusion model research. This tutorial will explain the principles of diffusion models and their application to solving other problems so that scientists and graduate students can better understand and apply these models.

Article link: https://arxiv.org/abs/2403.18103

This tutorial consists of four parts covering support diffusion in recent research literature Some basic concepts of generative models: Variational Autoencoder (VAE), Denoising Diffusion Probabilistic Model (DDPM), Langevin Dynamics Score Matching (SMLD) and SDE. These models independently derive the same diffusion ideas from multiple perspectives and are 50 pages long.

Introduction to the author

The author of this tutorial is Elmore Associate Professor, School of Electrical and Computer Engineering and Department of Statistics, Purdue University, USA Stanley H. Chan.

In 2007, Stanley Chan received his bachelor's degree from the University of Hong Kong, and then obtained his master's degree in mathematics and PhD in electrical engineering from the University of Canada, San Diego in 2009 and 2011 respectively. From 2012 to 2014, he served as a postdoctoral fellow at the Harvard John A. Paulson School of Engineering and Applied Sciences. Joined Purdue University in 2014.

Stanley Chan is mainly engaged in computational imaging research. His research mission is to build smart cameras by co-designing sensors and algorithms to enable visibility in all imaging conditions.

Stanley Chan has also won multiple paper awards, including the 2022 IEEE Signal Processing Society (SPS) Best Paper Award, the 2016 IEEE International Conference on Image Processing (ICIP) Best Paper Award, etc.

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