A Primary Comparison of Diffusion Models and Generative Adversarial Networks for Image Synthesis
Abstract
The aim of this paper is to investigate the application of different types of datasets on image generation models, specifically the MINIST dataset and the CIFAR-10 dataset, and experiments were conducted using Diffusion Models and Generative Adversarial Networks (GANs) models. The performance and training process are evaluated and analyzed by comparing the two generative models for image synthesis. Through these comparison experiments, we find that both models have impressive performance in image generation. Specifically, Diffusion Models have a more stable training process and perform better in the later stages of training, while GANs have a shorter training time but are relatively less stable due to their adversarial training approach, and have more prominent generation results in the early stages but are slightly weaker than Diffusion Models in the later stages. These findings help me better understand and compare the characteristics and applicability scenarios of different generative models and applicable scenarios.
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