Abstract: Few-Shot Learning(FSL) aims to build efficient predictive models using only a small number of labeled samples,thereby reducing the reliance on large-scale annotated data and improving the learning efficiency and practical value of models.How-ever,when there is a significant distribution shift between the test domain and the training domain,traditional methods often suffer a severe performance drop due to domain shift.Existing few-shot learning methods designed for domain generalization scenarios mostly rely on specific model architectures or alignment strategies,making them difficult to integrate with other methods to enhance generalization capabilities.Moreover,they often struggle to balance the learning of task-relevant features and domain-inva-riant features.To address these issues,this paper proposes the Model-agnostic Cross-domain Few-shot Learning framework based on the strategy of Invariant Risk Minimization(IRM).This framework can be integrated with various existing few-shot learning methods,enabling these models to effectively learn domain-invariant features from samples,thereby significantly improving their cross-domain predictive performance.Experiments on multiple benchmark datasets demonstrate the effectiveness of the proposed framework.

Key words: Few-shot learning, Cross-domain learning, Invariant risk minimization, Meta-learning, Data efficiency