MLFD: A Novel Meta-Learning Method with Fourier Transform Data Augmentation for Domain Generalization

As Machine Learning (ML) and Artificial Intelligence (AI) progress rapidly, the issue of ML model generalization has emerged as a critical concern for academics and practitioners alike. In practical scenarios, it is essential for models to sustain high performance when encountering varied and novel data distributions. Nevertheless, current domain generalization techniques have their shortcomings in tackling this challenge. The objective of this paper is to introduce a novel Meta-Learning approach, incorporating Fourier transform-based Data augmentation, called MLFD, for the purpose of domain generalization. Utilizing both data augmentation and a meta-learning architecture, this proposed technique empowers models to extend their generalization to multiple unseen target domains using just a single training domain. In contrast to other domain generalization methods, the method presented in this paper achieves comparable accuracy on the Digits-DG datasets, and demonstrates substantial improvements in terms of reducing model training time.