| { |
| "field_id": "F018", |
| "field": "anomaly detection", |
| "directions": [ |
| { |
| "direction_id": "G0", |
| "name": "Normal-content restoration for anomaly detection", |
| "scope": "Obtain a normal counterpart of an observation through reconstruction, completion, or denoising, and detect or localize anomalies from the differences." |
| }, |
| { |
| "direction_id": "G1", |
| "name": "Temporal expectation and pattern deviations", |
| "scope": "Use expected states of temporal behavior or observed recurring patterns as references to identify anomalous periods, trajectories, or subsequent observations." |
| }, |
| { |
| "direction_id": "G2", |
| "name": "Distributional and sample-geometric outlier scoring", |
| "scope": "Directly assign anomaly scores based on probabilistic evidence for observations under a normal distribution, or on their sparse or isolated positions relative to other samples." |
| }, |
| { |
| "direction_id": "G3", |
| "name": "Anomaly-discriminative feature learning and references", |
| "scope": "Make a feature space distinguish normal structure from deviations, and use normal feature references or learned representations for detection and localization." |
| }, |
| { |
| "direction_id": "G4", |
| "name": "Learning with limited anomaly evidence", |
| "scope": "When real anomaly examples are absent or scarce, construct surrogate anomalies, use a few known anomalies, or selectively obtain expert feedback to learn more targeted detectors." |
| }, |
| { |
| "direction_id": "G5", |
| "name": "Structural and relational anomaly detection", |
| "scope": "Use relationships among entities, attributes, or concurrently varying variables to determine whether structural or relational behavior deviates from normal, and localize the deviation where applicable." |
| }, |
| { |
| "direction_id": "G6", |
| "name": "Video behavior anomaly recognition and localization", |
| "scope": "Recognize anomalous behavior from actions, appearance, scenes, and their spatiotemporal relationships in video, and identify anomalous segments when needed." |
| }, |
| { |
| "direction_id": "G7", |
| "name": "Anomaly capability transfer across target settings", |
| "scope": "When target-specific anomaly training is limited or source and target conditions differ, transfer existing knowledge to maintain anomaly recognition and localization capability in the target setting." |
| }, |
| { |
| "direction_id": "G8", |
| "name": "Sustained anomaly monitoring under deployment constraints", |
| "scope": "Maintain timely and usable anomaly monitoring on continuously arriving data despite changing conditions or computational, storage, or device constraints." |
| }, |
| { |
| "direction_id": "G9", |
| "name": "Anomaly explanation and attribution", |
| "scope": "Provide usable feature attributions, explanations of causes, or counterfactual explanations of what would make flagged anomalies normal." |
| }, |
| { |
| "direction_id": "G10", |
| "name": "Detector configuration and combination", |
| "scope": "When candidate detectors or configurations are uncertain, select, tune, or combine candidate approaches to obtain more reliable anomaly judgments." |
| }, |
| { |
| "direction_id": "G11", |
| "name": "Anomaly detection evaluation and assessment", |
| "scope": "Use benchmarks, metrics, or systematic comparisons to assess anomaly detectors’ capabilities, limitations, and evaluation conclusions, rather than directly selecting a detector for a particular dataset." |
| } |
| ] |
| } |
|
|
