cs.NI · 2606.10827 Copy arXiv ID · Jun 9, 2026 Save A Unified Siamese Learning Framework for Zero-Day Anomaly Detection and Classification in Optical Networks Authors: Carlos Natalino , Flávia P. Monteiro , Paolo Monti
Organizations: Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden · Federal University of Western Par´a (UFOPA), 68040-255 Santar´em, Par´a, Brazil
Abstract A multi-similarity Siamese neural network unifies zero-day anomaly detection and one-shot classification in optical networks, achieving over 99% accuracy and instant adaptability across lightpaths and unseen anomaly types without any retraining.
Explore similar work May 22, 2026 · Huan Wang, Jun Shen, Jun Yan +1 Anomaly Detection Detection Framework
Jun 29, 2026 · Yousuf Moiz Ali, Jaroslaw E. Prilepsky, João Pedro +4 Learning-Augmented Algorithms Active Learning
May 22, 2026 · cs.CV J/K move · Enter open · S save
Huan Wang, Jun Shen, Jun Yan, Guansong Pang
Singapore Management University, Singapore · University of Wollongong, Australia
This paper considers a practical few-shot anomaly detection (FSAD) setting, termed discriminative FSAD, where a limited number of both normal and anomalous examples are available as references during inference. Existing FSAD methods rely on normal-only references through normality matching, ignoring the discriminative clues in anomalous references, while directly fitting both references can overfit to the seen anomalies. We introduce IDEAL, an intrinsic deviation learning framework that leverages both reference types to learn intrinsic deviation patterns characterizing generalizable abnormality as deviations from normality. IDEAL decomposes the learning process into two novel components: 1) a Normal Variation Eraser to suppress nuisance normal variations that may lead to noisy deviations from normality, thereby highlighting anomaly-relevant deviation representations; 2) an Intrinsic Deviation Encoder to decompose these denoised deviation representations into intrinsic deviation vectors capturing the most discriminative orthogonal deviation directions. At inference, IDEAL scores query-to-normal deviations preserved after projection onto the learned intrinsic deviation vectors, enabling generalization for both seen and unseen anomalies. Extensive experiments on eight real-world datasets show that IDEAL generalizes effectively to unseen anomalies and consistently outperforms existing state-of-the-art FSAD methods. Code and data will be available at \href{https://github.com/mala-lab/IDEAL}{https://github.com/mala-lab/IDEAL}.