Data-Centric Meta-Learning for Robust Few-Shot Generalization

Few-shot learning aims to enable rapid adaptation to unseen tasks using limited data. Optimization-based meta-learning addresses this challenge by acquiring shared prior knowledge across diverse tasks. However, its effectiveness degrades in cross-domain scenarios where unseen tasks differ significantly from training tasks. We identify this degradation as a failure to acquire generalizable prior knowledge, which is fundamentally caused by gradient discrepancies—conflicting update directions arising in the meta-training environment with diverse task distributions. To achieve robust few-shot generalization, we propose Data-Centric Meta-Learning (DCML), a novel framework that mitigates gradient discrepancies by aligning task-specific input distributions with shared prior knowledge. DCML accomplishes this alignment through a meta-learnable visual prompt that is integrated into the entire meta-learning process—unlike previous prompt-based methods restricted solely to test-time adaptation. During meta-training, the prompt transforms each task’s inputs to induce more consistent gradients, thereby facilitating the learning of generalizable prior knowledge. Leveraging this robust knowledge, DCML enables rapid and parameter-efficient test-time adaptation by updating only the lightweight prompt and classifier while keeping the backbone frozen.Extensive experiments demonstrate that DCML consistently outperforms baselines, particularly in challenging few-shot cross-domain scenarios, establishing a data-centric perspective for robust meta-learning.