Swiss Army VAE: comprehensive sensor data analysis via explainable AI

John Helmsen; Brian Baracat-Donovan; Riley White; Thomas Cullough

doi:10.1117/12.2680317

17 October 2023 Swiss Army VAE: comprehensive sensor data analysis via explainable AI

John Helmsen, Brian Baracat-Donovan, Riley White, Thomas Cullough

Proceedings Volume 12742, Artificial Intelligence for Security and Defence Applications; 127420I (2023) https://doi.org/10.1117/12.2680317
Event: SPIE Security + Defence, 2023, Amsterdam, Netherlands

Abstract

An integrated system for processing sensor data has been developed based on novel variational autoencoder (VAE) algorithms with explainability that significantly eases analysis of sensor data. By continuously updating a generative model of the data, the system assists users with minimal artificial intelligence (AI) training or experience to perform data analysis. The system performs an extensive range of integrated machine learning (ML) tasks: anomaly detection, active learning, model-drift detection, synthetic data generation, semi-supervised classification, and counterfactual explanation generation. When the system is provided a data schema (map of Booleans, integers, reals, categories, time series, etc.) and data set, it automatically forms a preliminary generative model of the data. The construction of the system is modular, so new data types can be added as necessary. Counterfactually explainable anomaly detection is immediately performed via sparse gradient search. This informs the user how to interactively remove or repair bad records and/or begin labeling records of interest. The addition of labels to the data allows multi-class, semi-supervised, counterfactually explainable classification via the support vector machine embedded hyperplane algorithm (SVM-EH). Once some labels are added, active learning is used to assist further labeling by suggesting data elements that are highly likely to improve classification accuracy, significantly accelerating the labeling process by trading human effort for computational cycles. In production, the system detects when its training is becoming stale and requests retraining.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

John Helmsen, Brian Baracat-Donovan, Riley White, and Thomas Cullough "Swiss Army VAE: comprehensive sensor data analysis via explainable AI", Proc. SPIE 12742, Artificial Intelligence for Security and Defence Applications, 127420I (17 October 2023); https://doi.org/10.1117/12.2680317

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KEYWORDS

Data modeling

Sensors

Artificial intelligence

Data analysis

Army

Classification systems

Data processing

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