Online Deep Sets for Transfer Anomaly Detection
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Solution Overview
Problem
Conventional anomaly detection techniques require extensive training on target domains, are limited by the need for large datasets, and lack online learning capabilities, making them inefficient for real-time anomaly detection.
Innovation Solution
The use of online deep sets to compute domain vectors for target domain samples, allowing for anomaly detection without retraining on the target domain, and enabling incremental updates with minimal target samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional anomaly detection techniques are used, then training can be performed with source domain samples, but the system requires extensive retraining on target domain data and large datasets, reducing efficiency and increasing time requirements
Solution Approach 1:
The system performs preliminary action by pre-training the anomaly detection model on source domain data before deployment. The pre-trained model includes initialized parameters that can be rapidly adapted to target domains using only a small number of target samples, eliminating the need for extensive target domain training and significantly reducing deployment time
Solution Approach 2:
The system uses parameter copying from source domain training to target domain deployment. The model parameters learned from source domain data are copied and transferred to the target domain, where they serve as a strong initialization that requires minimal adjustment with just a few target samples, thus improving efficiency while reducing time loss
2Adaptability or versatility
If conventional anomaly detection techniques are used, then training can be performed offline with large datasets, but the system lacks online learning capability, reducing adaptability to new domains
Solution Approach 1:
The system implements dynamic online learning capability where the anomaly detection model can continuously adapt to new target domains in real-time. The model accepts incremental updates with streaming data from target domains, dynamically adjusting its parameters without requiring complete retraining, thus enabling adaptability while reducing data requirements to minimal samples
Solution Approach 2:
The system changes parameters incrementally through online learning mechanisms. Instead of fixed offline-trained parameters, the model continuously updates its parameters based on incoming target domain samples, allowing adaptability to new domains while requiring only minimal data for parameter adjustment
3Ease of manufacture
If conventional anomaly detection techniques are used, then training can be performed with large datasets, but the system requires extensive target domain data, increasing device complexity and data storage requirements
Solution Approach 1:
The system extracts and transfers only the essential anomaly detection capabilities from source domain training to target domain deployment. By separating the core detection logic from domain-specific data requirements, the system achieves easy deployment with minimal target domain data, extracting only the necessary parameters and knowledge needed for effective anomaly detection in new domains
Data Source
AI summary
A computerized machine learning anomaly detection model trained on a plurality of samples of one or more source domains (optionally, one or more source domains and the target domain) is accessed. Using online deep sets, one or more domain vectors are computed for each target domain sample at an observation point, each target domain sample corresponding to a given target domain, where the one or more domain vectors represent a similarity and difference among the source and target domains. The target domain sample is processed using the anomaly detection model trained on the plurality of samples of the source to generate an anomaly score, the processing being based on the computed one or more domain vectors.