Rarity-Gated Context Conditioning for Offline Imitation Learning-Based Maritime Anomaly Detection
arXiv:2606.13311v1 Announce Type: cross Abstract: Contextual anomaly detection aims to identify abnormal behavior conditional on context variables, but practical deployments often face highly imbalanced context distributions where rare regimes can be critical information. Under such frequency bias, context-conditioned models can produce unstable decisions and excessive false alarms in rare contexts. We propose Rarity-Gated Feature-wise Linear Modulation (RGFiLM), a rarity-aware conditioning module that combines feature-wise modulation (i.e., context-conditioned scaling and shifting of hidden f
The increasing deployment of AI in complex, real-world systems like maritime anomaly detection, especially in environments with highly imbalanced data distributions, necessitates robust solutions for rare event conditioning.
This development addresses a critical limitation in AI's ability to operate reliably in dynamic, high-stakes contexts, where rare but critical events are often misclassified, thereby improving system integrity and decision-making.
The ability to accurately detect anomalies in rare contexts using rarity-gated conditioning can lead to more stable and trustworthy AI deployments in fields requiring high precision and low false-alarm rates.
- · Maritime logistics and shipping
- · Defense and security sectors (surveillance)
- · AI/ML model developers
- · Insurance and risk management
- · Traditional anomaly detection systems
- · Organizations reliant on human-intensive monitoring for rare events
Improved reliability and reduced false positives in AI-driven anomaly detection systems across various industries.
Accelerated adoption of AI in critical infrastructure monitoring and security applications due to enhanced trustworthiness.
Enhanced global supply chain security and reduced operational risks via more accurate detection of illicit activities or anomalies.
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Read at arXiv cs.AI