Enhanced diffusion model based on similarity for handwritten digit generation
Wenjing Kang, Wenbo Li · Applied and Computational Engineering · 2024
In recent years with the rise of deep learning, there has been a major revolution in image generation technology. Deep learning models, especially the diffusion model. have brought about breakthrough progress in image generation. Various deep generation models have recently demonstrated a wide variety of high-quality sample data patterns. Although image generation technology has achieved remarkable achievement. There are still challenges and issues, such as quality control in generated images. In order to improve the robustness and performance of diffusion model in image generation, an enhanced diffusion model based on similarity is proposed in this paper. Based on the original diffusion model, the similarity loss function is added to narrow the semantic distance between the original image and the generated image, so that the generated image is more robust. Extensive experiments were carried out on the MINIST dataset, and the experimental results showed that compared with the other generation models, the enhanced diffusion model based on similarity obtained the best scores of IS=31.61 and FID=175.21, which verified the validity of the similarity loss.