GAN Discriminator Anomaly Detection Without Labeled Data
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Solution Overview
Problem
Deep neural networks require large amounts of labeled data to detect anomalies, which is time-consuming and expensive due to the skewness of data towards normal cases and the need for human expert labeling.
Innovation Solution
A system utilizing a Generative Adversarial Network (GAN) for anomaly detection, where a discriminator is trained to classify real-world information into anomalies without requiring explicit labels, using synthetic and real samples to determine anomalies such as glass break sounds or gunshots, and triggering actions when anomalies are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep neural networks are trained to detect anomalies, then anomaly detection capability is improved, but data collection time and cost increase significantly
Solution Approach 1:
The patent uses synthetic data generated by generative models as copies of real anomaly data. These synthetic samples replicate the characteristics of actual anomalies (glass break, gunshot, baby cry, falling body, emergency siren) without requiring physical collection of every possible anomaly instance, thus reducing data collection time while maintaining detection capability
Solution Approach 2:
The system uses unsupervised learning where the model learns normal patterns automatically without human-labeled anomaly data. The anomaly detection emerges self-organically from the learned representation of normal behavior, eliminating the need for time-consuming manual annotation of anomaly cases
2Measurement precision
If deep neural networks are trained with large amounts of labeled data, then detection accuracy is improved, but expert labeling cost increases
Solution Approach 1:
The system employs unsupervised learning approaches where the model automatically learns to distinguish anomalies from normal patterns without human expert intervention for labeling. The learning process self-organizes feature representations that enable accurate anomaly detection while eliminating expensive manual annotation
Solution Approach 2:
Synthetic anomaly data is generated programmatically to replace the need for expert-labeled real anomaly data. These synthesized samples preserve the essential characteristics of anomalies while being generated automatically, thus eliminating the need for expensive expert labeling
3Reliability
If real-world anomaly data is collected extensively, then training data quality is improved, but data skewness towards normal cases persists
Solution Approach 1:
The system generates synthetic anomaly data that balances the training dataset. By creating realistic anomaly samples through generative models, the training data achieves a balanced distribution between normal and anomaly cases, overcoming the inherent skewness that would otherwise persist even with extensive real data collection
Data Source
AI summary
In an embodiment, an apparatus includes: a sensor to sense real world information; a digitizer coupled to the sensor to digitize the real world information into digitized information; a signal processor coupled to the digitizer to process the digitized information into an image; a discriminator coupled to the signal processor to determine, based at least in part on the image, whether the real world information comprises an anomaly, where the discriminator is trained via a generative adversarial network; and a controller coupled to the discriminator.