A Comprehensive Augmentation Framework for Anomaly Detection

2023-08-29

AAAI Conference on Artificial Intelligence

AI Summary

Existing anomaly detection models suffer from training bias and degraded generalization due to the lack of a unified standard for anomaly modeling. This paper addresses the limitation of reconstruction-based methods—specifically, their failure to account for inter-class discrepancies in anomaly characteristics during data augmentation—by proposing the first composable augmentation framework tailored for reconstruction models. We first systematically identify key factors by which synthetic anomalies influence reconstruction training. Then, we design a class-aware augmentation composition mechanism coupled with a decoupled, multi-stage training strategy comprising feature-space perturbation, class-conditional augmentation selection, and parameter freezing. On MVTec-AD, our method significantly outperforms state-of-the-art approaches, especially in object-level anomaly detection. Moreover, on a newly constructed multi-characteristic synthetic anomaly benchmark, it demonstrates superior cross-class generalization capability.

Abstract

Data augmentation methods are commonly integrated into the training of anomaly detection models. Previous approaches have primarily focused on replicating real-world anomalies or enhancing diversity, without considering that the standard of anomaly varies across different classes, potentially leading to a biased training distribution. This paper analyzes crucial traits of simulated anomalies that contribute to the training of reconstructive networks and condenses them into several methods, thus creating a comprehensive framework by selectively utilizing appropriate combinations. Furthermore, we integrate this framework with a reconstruction-based approach and concurrently propose a split training strategy that alleviates the overfitting issue while avoiding introducing interference to the reconstruction process. The evaluations conducted on the MVTec anomaly detection dataset demonstrate that our method outperforms the previous state-of-the-art approach, particularly in terms of object classes. We also generate a simulated dataset comprising anomalies with diverse characteristics, and experimental results demonstrate that our approach exhibits promising potential for generalizing effectively to various unseen anomalies encountered in real-world scenarios.

Problem

• Anomaly Detection
• Model Training
• Generalization Bias

Innovation

• Comprehensive Framework
• Anomaly Detection
• Segmentation Training