SPRINT: Semi-supervised Prototypical Representation for Few-Shot Class-Incremental Tabular Learning
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 en...
| Authors | Umid Suleymanov, Murat Kantarcioglu, Kevin S Chan et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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