[2603.04321] SPRINT: Semi-supervised Prototypical Representation for Few-Shot Class-Incremental Tabular Learning

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.04321 (cs) [Submitted on 4 Mar 2026] Title:SPRINT: Semi-supervised Prototypical Representation for Few-Shot Class-Incremental Tabular Learning Authors:Umid Suleymanov, Murat Kantarcioglu, Kevin S Chan, Michael De Lucia, Kevin Hamlen, Latifur Khan, Sharad Mehrotra, Ananthram Swami, Bhavani Thuraisingham View a PDF of the paper titled SPRINT: Semi-supervised Prototypical Representation for Few-Shot Class-Incremental Tabular Learning, by Umid Suleymanov and 8 other authors View PDF HTML (experimental) Abstract: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...