Application of conditional DDPM on the MNIST dataset
Xin Wang · 2025
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.