Meta-DM: Applications of Diffusion Models on Few-Shot Learning
Wentao Hu Xiurong Jiang Jiarun Liu Yuqi Yang Hui Tian
In the field of few-shot learning (FSL), extensive research has focused on
improving network structures and training strategies. However, the role of data
processing modules has not been fully explored. Therefore, in this paper, we
propose Meta-DM, a generalized data processing module for FSL problems based on
diffusion models. Meta-DM is a simple yet effective module that can be easily
integrated with existing FSL methods, leading to significant performance
improvements in both supervised and unsupervised settings. We provide a
theoretical analysis of Meta-DM and evaluate its performance on several
algorithms. Our experiments show that combining Meta-DM with certain methods
achieves state-of-the-art results.