SPRINT: 为少样本类增量学习的半监督原型表示
SPRINT: Semi-supervised Prototypical Representation for Few-Shot Class-Incremental Tabular Learning
AI总结：
SPRINT提出了一种针对表格数据的少样本类增量学习框架，通过混合经验训练策略和低存储成本保留基础类历史，实现了跨领域鲁棒性，达到77.37%的平均准确率。
摘要：
现实系统必须在有限数据下持续适应新概念，同时不遗忘之前获得的知识。尽管少样本类增量学习（FSCIL）在计算机视觉中已建立，但其在表格领域中的应用仍 largely 未被探索。与图像不同，表格流（例如日志、传感器）提供了丰富的未标记数据，专家注释稀缺，存储成本极低，这些特征被现有基于视觉的方法所忽视，这些方法依赖于限制性的缓冲区。我们引入SPRINT，第一个专门针对表格分布的FSCIL框架。SPRINT引入了一种混合经验训练策略，利用基于置信度的伪标签来丰富新类表示，并利用低存储成本保留基础类历史。在六个跨越网络安全、医疗和生态领域的多样化基准上的广泛评估表明，SPRINT的跨域鲁棒性。它在5-shot情况下实现了最先进的平均准确率为77.37%，优于最强的增量基线4.45%。
英文摘要：
Real-world systems must continuously adapt to novel concepts from limited data without forgetting previously acquired knowledge. While Few-Shot Class-Incremental Learning (FSCIL) is established in computer vision, its application to tabular domains remains largely unexplored. Unlike images, tabular streams (e.g., logs, sensors) offer abundant unlabeled data, a scarcity of expert annotations and negligible storage costs, features ignored by existing vision-based methods that rely on restrictive buffers. We introduce SPRINT, the first FSCIL framework tailored for tabular distributions. SPRINT introduces a mixed episodic training strategy that leverages confidence-based pseudo-labeling to enrich novel class representations and exploits low storage costs to retain base class history. Extensive evaluation across six diverse benchmarks spanning cybersecurity, healthcare, and ecological domains, demonstrates SPRINT's cross-domain robustness. It achieves a state-of-the-art average accuracy of 77.37% (5-shot), outperforming the strongest incremental baseline by 4.45%.
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