Year: 2025
Type: article
Abstract: We provide an overview of the diffusion model as a method to generate new samples.Generative models have been recently adopted for tasks such as art generation (Stable Diffusion, Dall-E) and text generation (ChatGPT).Diffusion models in particular apply noise to sample data and then "reverse" this noising process to generate new samples.We will formally define these noising and denoising processes, then present algorithms to train and generate with a diffusion model.Afterward, we will explore a potential application of diffusion models in improving classifier performance on imbalanced data.
DOI: 10.1137/24s1717993
Author: Justin Le
Institution: Arizona State University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: Arizona State University
OpenAlex ID: W4411713463