Application of conditional DDPM on the MNIST dataset

Xin Wang

doi:10.1117/12.3053085

3 January 2025 Application of conditional DDPM on the MNIST dataset

Xin Wang

Author Affiliations +

Proceedings Volume 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024); 134420G (2025) https://doi.org/10.1117/12.3053085
Event: Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 2024, Kaifeng, China

Abstract

Since its introduction, Denoising Diffusion Probabilistic Models (DDPM) have received widespread attention for their exceptional performance in image generation. They generate new samples by simulating the denoising process of data, a method that is not only simple and efficient but also capable of producing highly realistic samples. This paper explores the application of Conditional Denoising Diffusion Probabilistic Models (Conditional DDPM) on the MNIST dataset. MNIST is a classic dataset containing handwritten digit images, widely used in computer vision and machine learning fields. The paper first introduces the basic principles and model structure of Conditional DDPM, then elaborately explains how to train and apply the Conditional DDPM on the MNIST dataset, and analyzes the experimental results. The experimental results show that the Conditional DDPM can generate high-quality handwritten digit images that meet specific conditions on the MNIST dataset.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xin Wang "Application of conditional DDPM on the MNIST dataset", Proc. SPIE 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 134420G (3 January 2025); https://doi.org/10.1117/12.3053085

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KEYWORDS

Image processing

Denoising

Data modeling

Diffusion

Education and training

Image restoration

Machine learning

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