Rarity-Gated Context Conditioning for Offline Imitation Learning-Based Maritime Anomaly Detection

2026-06-12 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

Rarity-Gated Feature-wise Linear Modulation (RGFiLM) is a novel conditioning module designed to address frequency bias in contextual anomaly detection, particularly in scenarios with highly imbalanced context distributions where rare regimes are critical. Proposed by Kim et al. from UNIST, RGFiLM integrates Feature-wise Linear Modulation (FiLM) with a data-driven rarity score, estimated from empirical context distributions, to adaptively control context influence. The gate becomes more decisive in rare contexts and remains conservative in frequent ones. Evaluated on maritime trajectory anomaly detection using AIS motion sequences with ERA5 environmental context in an environment-sensitive detour scenario, RGFiLM achieved the best mean F1–False Positive Rate (FPR) trade-off. It demonstrated a mean F1 score of 0.595 and a mean FPR of 0.097, outperforming context-agnostic (F1 0.524, FPR 0.120) and standard context-conditioned baselines.

Key takeaway

For Machine Learning Engineers deploying contextual anomaly detection in safety-critical domains with imbalanced context distributions, you should consider implementing rarity-aware conditioning like RGFiLM. This approach mitigates "frequency bias" by adaptively modulating context influence, leading to a better F1–FPR trade-off and significantly reducing false positives in rare but crucial environmental conditions. Integrating RGFiLM can enhance the robustness and reliability of your anomaly detection systems.

Key insights

Explicitly accounting for context rarity reduces false alarms and improves robustness in contextual anomaly detection.

Principles

• Frequency bias degrades contextual anomaly detection.
• Rarity-aware gating calibrates context influence.
• Sharper context modulation benefits rare environments.

Method

RGFiLM computes a rarity score (e.g., Mahalanobis distance) from context variables, then uses a rarity-dependent gate to interpolate between context-agnostic and FiLM-modulated representations.

In practice

• Apply RGFiLM to sequential decision models.
• Use Mahalanobis distance for rarity scoring.
• Integrate with offline imitation learning frameworks.

Topics

• Contextual Anomaly Detection
• Rarity-Gated FiLM
• Maritime Trajectory Analysis
• Offline Imitation Learning
• Frequency Bias
• AIS Data
• ERA5 Environmental Data

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.