让少样本学习更稳、更抗噪、更通用，适合医疗等数据少的场景。
Adaptive Few-Shot Learning (AFSL): Tackling Data Scarcity with Stability, Robustness, and Versatility
- 动态稳定性模块提升模型一致性
- 多模态融合与抗噪设计增强鲁棒性
- 适合医疗、机器人等高风险少数据领域
少样本学习（FSL）使模型能在少量标注数据下有效泛化，对医疗、机器人和自然语言处理等数据稀缺领域至关重要。然而，现有方法仍存在对初始化敏感、跨域适应难、易受噪声数据影响等问题。本文提出自适应少样本学习（AFSL）框架，整合元学习、领域对齐、抗噪能力与多模态融合技术，包含四个核心模块：动态稳定性模块保障性能一致，上下文域对齐模块实现跨域适应，噪声自适应韧性模块处理噪声数据，多模态融合模块支持异构信息整合。此外，还引入任务感知数据增强、半监督学习与可解释AI策略，显著提升FSL在真实场景中的可靠性与实用性。该框架为高风险、数据稀缺领域提供可扩展、稳健且有效的解决方案。
原文摘要 · Abstract (English)
Few-shot learning (FSL) enables machine learning models to generalize effectively with minimal labeled data, making it crucial for data-scarce domains such as healthcare, robotics, and natural language processing. Despite its potential, FSL faces challenges including sensitivity to initialization, difficulty in adapting to diverse domains, and vulnerability to noisy datasets. To address these issues, this paper introduces Adaptive Few-Shot Learning (AFSL), a framework that integrates advancements in meta-learning, domain alignment, noise resilience, and multi-modal integration. AFSL consists of four key modules: a Dynamic Stability Module for performance consistency, a Contextual Domain Alignment Module for domain adaptation, a Noise-Adaptive Resilience Module for handling noisy data, and a Multi-Modal Fusion Module for integrating diverse modalities. This work also explores strategies such as task-aware data augmentation, semi-supervised learning, and explainable AI techniques to enhance the applicability and robustness of FSL. AFSL provides scalable, reliable, and impactful solutions for real-world, high-stakes domains.
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