Anomaly Augmented GAN for False Positive Reduction
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Solution Overview
Problem
Existing anomaly detection methods using generative models struggle to guarantee the correct identification of anomalous data, often resulting in false positives, as they focus on modeling normal data distributions without ensuring effective detection of anomalies.
Innovation Solution
The proposed Anomaly Augmented Generative Adversarial Network (AAGAN) employs a bi-directional GAN with a novel loss function and framework, incorporating a surrogate anomaly distribution and additional discriminators to adversarially train the generator and encoder, ensuring robust anomaly detection by distinguishing between normal and anomalous samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If generative models focus on modeling normal data distributions, then the reconstruction capability is improved, but the anomaly detection accuracy deteriorates due to false positives
Solution Approach 1:
The patent segments the training process into two distinct phases: first training the generator on normal data to learn the data distribution, then training a separate discriminator to distinguish normal from anomalous samples. This segmentation allows each component to specialize, improving anomaly detection accuracy while reducing false positives by preventing the generator from overfitting to normal patterns.
Solution Approach 2:
The discriminator serves as an intermediary component that mediates between the generator and the anomaly detection task. It provides feedback to the generator about reconstruction quality and introduces anomaly awareness into the training process, enabling the system to detect anomalies accurately without the generator becoming too specialized in normal data reconstruction.
2Loss of information
If the generator is trained to reconstruct normal data perfectly, then the representation of normal data is improved, but the ability to detect anomalies deteriorates
Solution Approach 1:
The patent introduces dynamic training stages where the generator's objective changes over time. In the first stage, the generator focuses on minimizing reconstruction error for normal data. In the second stage, the training dynamics shift as the discriminator is introduced, causing the generator to adapt by learning to produce reconstructions that the discriminator cannot easily distinguish from real normal data, thereby maintaining information fidelity while enabling anomaly detection.
Solution Approach 2:
The training process employs periodic alternation between training the generator and training the discriminator. This periodic action ensures that the generator continuously improves its reconstruction capability while the discriminator simultaneously improves its anomaly detection capability, preventing either objective from completely dominating the other.
3Ease of manufacture
If anomaly detection methods use only normal data for training, then the simplicity of training is improved, but the detection reliability deteriorates
Solution Approach 1:
The patent applies preliminary action by first training the generator exclusively on normal data to establish a solid understanding of the data distribution. Only after this preliminary training is complete is the discriminator introduced with anomalous samples. This staged approach maintains training simplicity in the initial phase while ultimately achieving reliable anomaly detection through the subsequent discriminator training.
Data Source
AI summary
Systems and methods for anomaly detection in accordance with embodiments of the invention are illustrated. One embodiment includes a method for training a system for detecting anomalous samples. The method draws data samples from a data distribution of true samples and an anomaly distribution and draws a latent sample from a latent space. The method further includes steps for training a generator to generate data samples based on the drawn data samples and the latent sample, and training a cyclic discriminator to distinguish between true data samples and reconstructed samples. A reconstructed sample is generated by the generator based on an encoding of a data sample. The method identifies a set of one or more true pairs, a set of one or more anomalous pairs, and a set of one or more generated pairs. The method trains a joint discriminator to distinguish true pairs from anomalous and generated pairs.