Multi-Modal Anomaly Detection via Out-of-Distribution Training
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Solution Overview
Problem
Existing machine learning models struggle to accurately detect anomalies in multi-modal data sets, particularly when the test data distribution differs from the training data distribution, leading to potential serious consequences in critical systems.
Innovation Solution
The use of machine learning techniques to analyze multi-modal data from a variety of sensors, including imaging, audio, network, and other modalities, to detect out-of-distribution data and identify root causes of anomalies, enabling timely remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained on training data to detect anomalies, then anomaly detection capability is improved, but the models fail when test data distribution differs from training data distribution
Solution Approach 1:
The system performs preliminary actions by training the machine learning model not only on normal training data but also by exposing it to out-of-distribution data during the training phase. This preliminary exposure enables the model to learn characteristics of both in-distribution and out-of-distribution data, making it more robust when encountering distribution shifts during actual anomaly detection operations.
2Measurement precision
If multi-modal data from multiple sensors is collected for anomaly detection, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system merges data from multiple sensors of different modalities (e.g., imaging, audio, network sensors) into a unified multi-modal data set. By combining these diverse data sources, the system achieves more accurate anomaly detection through complementary information, while the machine learning model integrates the multi-modal inputs into a cohesive analysis framework.
Solution Approach 2:
The machine learning model is designed with multi-functionality to handle various types of sensor data from different modalities. The model can process imaging data, audio data, network data, and other sensor inputs through unified processing pathways, enabling it to perform anomaly detection across multiple data types without requiring separate specialized systems for each modality.
3Productivity
If machine learning models process data quickly for anomaly detection, then response time is improved, but accuracy may deteriorate when data distribution changes
Solution Approach 1:
The system performs preliminary training with out-of-distribution data to pre-adapt the model for distribution shifts. This preliminary action ensures that when the model processes data quickly during operation, it maintains accuracy even when encountering unexpected data distributions, as the model has already learned to recognize and handle such variations during training.
Data Source
AI summary
A method performed by a processing system including at least one processor includes collecting a set of data from a plurality of sensors that is monitoring a system, wherein the plurality of sensors includes sensors of a plurality of different modalities, detecting an instance of out-of-distribution data in the set of data by providing the set of data as an input to a machine learning model that generates as an output an indicator that the instance of out-of-distribution data is out-of-distribution with respect to the set of data, identifying a root cause for the instance of out-of-distribution data, and initiating an action to remediate the root cause of the instance of out-of-distribution data.