Ranxin He

Application of a lightweight denoising diffusion model based on few-shot fine-tuning in handwritten digit generation

Diffusion models excel at generative imaging but necessitate extensive datasets and considerable computational power. Few-shot learning with these models frequently results in overfitting and inconsistent generative quality. This study addresses these challenges for resource-constrained diffusion models under severe da...