🤖 AI Summary
This work addresses the challenge of insufficient generalization in few-shot learning caused by distributional shifts. The authors propose a Prototype-Guided Distributionally Robust Optimization (PG-DRO) framework that integrates class-adaptive priors with optimal transport for the first time. By leveraging hierarchical optimal transport, PG-DRO learns structure-aware prototype priors from base-class data and embeds them into a Sinkhorn-based distributionally robust optimization formulation. This enables the dynamic construction of uncertainty sets aligned with transferable semantic structures. Extensive experiments demonstrate that PG-DRO significantly outperforms standard learners and existing DRO methods across multiple few-shot benchmarks, effectively enhancing model robustness and generalization under distributional shift.
📝 Abstract
Few-shot learning requires models to generalize under limited supervision while remaining robust to distribution shifts. Existing Sinkhorn Distributionally Robust Optimization (DRO) methods provide theoretical guarantees but rely on a fixed reference distribution, which limits their adaptability. We propose a Prototype-Guided Distributionally Robust Optimization (PG-DRO) framework that learns class-adaptive priors from abundant base data via hierarchical optimal transport and embeds them into the Sinkhorn DRO formulation. This design enables few-shot information to be organically integrated into producing class-specific robust decisions that are both theoretically grounded and efficient, and further aligns the uncertainty set with transferable structural knowledge. Experiments show that PG-DRO achieves stronger robust generalization in few-shot scenarios, outperforming both standard learners and DRO baselines.