Diffusion-Based Synthetic Outliers for Anomaly Detection Validation
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Solution Overview
Problem
Anomaly detection models face challenges in training and evaluation due to the lack of labeled anomalies, leading to biased performance estimation and under-explored evaluation methods, especially in new deployment environments where genuine validation datasets are absent.
Innovation Solution
A diffusion-based method generates synthetic outliers using pretrained models to create training and validation datasets for anomaly detection, leveraging diffusion-based generation techniques to enhance model performance and validation through outlier exposure and model fine-tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If anomaly detection models are trained without sufficient labeled anomaly data, then training can proceed with available normal data, but the model performance in detecting critical anomalies deteriorates
Solution Approach 1:
The patent creates synthetic copies of normal images by applying style transfer techniques to generate artificial anomaly samples. These synthetic anomaly images serve as proxies for real anomaly data, enabling the model to learn anomaly patterns without requiring actual labeled anomaly examples during training
Solution Approach 2:
The patent performs preliminary generation of synthetic anomaly data before model training. By pre-generating a diverse set of synthetic anomaly images with known labels, the system prepares training data in advance, allowing the anomaly detection model to be trained on both normal and synthetic anomaly samples, thereby improving detection accuracy before deployment
2Adaptability or versatility
If genuine validation datasets are not available in new deployment environments, then model deployment can proceed, but performance evaluation becomes biased or impossible
Solution Approach 1:
The patent generates synthetic validation datasets by applying style transfer to normal images from the deployment environment. These synthetic validation samples replicate the visual characteristics of potential anomalies in the target environment, enabling accurate performance evaluation without requiring real anomaly data from the deployment site
Solution Approach 2:
The patent introduces synthetic images as an intermediary between the model and real-world anomalies. These synthetic validation samples act as a bridge, allowing the model to be evaluated on anomaly-like patterns while using only normal images from the deployment environment, thus enabling unbiased performance measurement
Data Source
AI summary
A computer-implemented system and method relate to anomaly detection. A first source image is obtained from a first image set and a second source image is obtained from a second image set of an in-distribution image dataset. A diffusion model generates a modified image using the first source image and the second source image. A non-anomalous label is automatically generated for the first source image. The non-anomalous label is also generated for the second source image. An anomalous label is generated for the modified image. A training dataset is created. The training dataset includes at least the first source image with the non-anomalous label, the second source image with the non-anomalous label, and the modified image with the anomalous label. A machine learning model is trained or fine-tuned using the training dataset. The machine learning model being configured to perform a task of anomaly detection.