Application of Latent Diffusion Models (LDMs) in Data-Scarce Scenarios

College of Science, Shanghai University, Shanghai, China

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Abstract

The paper mainly focuses on the use of Latent Diffusion Models (LDMs) to address data scarcity, with an in-depth analysis of their practical performance in two important domains: medical imaging and modern industrial manufacturing. As a type of diffusion model, LDMs operate in low-dimensional latent spaces, avoiding the mode collapse issues of traditional Generative Adversarial Networks (GANs) while significantly reducing computational costs. In medical imaging, LDMs aid in generating high-quality, clinically relevant data while respecting privacy constraints; in industrial manufacturing, they support key tasks like enhancing defect detection by supplementing scarce defect samples. The paper further explores core challenges, including the lack of tailored evaluation criteria for LDM-generated images and risks to data privacy, like potential sensitive information leakage. Alongside this, the paper outlines future optimization directions, covering improvements to LDMs’ generalization capabilities and the development of more suitable assessment metrics for data-scarce scenarios so as to drive practical applications better.