Anomaly Detection in Unknown Domains Using Compressed Data
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Solution Overview
Problem
Anomaly detection in datasets where the domain setting is unknown and the neural network is not trained with anomalous data poses a challenge, as existing deep learning algorithms require large amounts of labeled data, which is often not available.
Innovation Solution
A neural network architecture that generates content-irrelevant and domain-irrelevant compressed data using multi-stage encoding and disentanglement techniques, allowing for zero-shot anomaly detection by suppressing domain and content information, and leveraging self-supervised learning to identify anomalous data without labeled training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing deep learning algorithms are used for anomaly detection, then detection accuracy can be improved, but large amounts of labeled training data are required which are often not available
Solution Approach 1:
The system performs self-supervised learning where the model learns to compress and reconstruct normal data patterns without requiring external labeled training data. The reconstruction loss itself serves as the learning signal, allowing the system to autonomously learn anomaly detection capabilities from unlabeled operational data.
Solution Approach 2:
The system extracts only the essential normal data patterns by compressing them into latent representations using autoencoders. By removing redundant information and keeping only the core structural patterns in the compressed space, the system can detect anomalies as deviations from these extracted normal patterns without needing labeled examples.
2Device complexity
If the neural network is trained only with normal data, then the model complexity is reduced, but the ability to detect anomalies in unknown domains is limited
Solution Approach 1:
The system introduces domain-irrelevant and content-irrelevant compressed data as intermediary representations. These intermediaries capture the essential structure of normal data while filtering out domain-specific variations, allowing the model to generalize anomaly detection across unknown domains without being tied to specific training domain characteristics.
Solution Approach 2:
The training process is segmented into two independent components: (1) learning normal data patterns through compression and reconstruction, and (2) detecting anomalies as deviations from these patterns. This segmentation allows the model to be trained solely on normal data while maintaining the capability to detect various types of anomalies without requiring anomalous training examples.
3Manufacturing precision
If domain-specific information is included in the compressed data, then the reconstruction accuracy is improved, but the anomaly detection capability across different domains is reduced
Solution Approach 1:
The system extracts and removes domain-specific information from the compressed representation by using domain-irrelevant compression. The autoencoder learns to compress data while deliberately excluding domain characteristics, keeping only the fundamental structural patterns that are common across all domains. This extracted core structure enables both accurate reconstruction and cross-domain anomaly detection.
Solution Approach 2:
The compressed representation is designed to have different quality characteristics for different purposes: it maintains sufficient fidelity for reconstruction tasks while simultaneously being domain-agnostic for detection tasks. The latent space is structured to preserve local data manifold relationships needed for accurate reconstruction while being invariant to domain-specific variations.
Data Source
AI summary
Embodiments of the invention describe a computer-implemented method of detecting anomalous data associated with a system-under-analysis. The computer-implemented method includes using a first encoder stage of a neural network to generate content-irrelevant latent code from input data. A second encoder stage of the neural network is used to generate domain-irrelevant latent code from the input data. A decoder stage of the neural network is used to generate reconstructed input data. The reconstructed input data includes a reconstruction of the input data based at least in part on the content-irrelevant latent code and the domain-irrelevant latent code. A reconstruction loss is generated based at least in part on the reconstructed input data. The reconstruction loss is used to determine that the input data includes an anomalous data candidate.