Meta-DM: Applications of Diffusion Models on Few-Shot Learning

TL;DR

Meta-DM introduces a diffusion model-based data processing module for few-shot learning, significantly enhancing existing methods' performance in supervised and unsupervised scenarios.

Contribution

It presents Meta-DM, a novel, easily integrable data processing module based on diffusion models, improving few-shot learning results.

Findings

Achieves state-of-the-art results when combined with existing algorithms.

Provides theoretical analysis supporting Meta-DM's effectiveness.

Enhances performance in both supervised and unsupervised few-shot learning.

Abstract

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.