[4S2-GS-2] Machine learning:

[4S2-GS-2-01] Integrated Method Using Gaussian Distribution Combination for Anomaly Detection

〇Seiya Iwamoto¹, Teng-Yok Lee¹, Ken Miyamoto¹, Akira Minezawa¹ (1. Mitsubishi Electric Corporation)

Keywords:Anomaly Detection, Unsupervised Learning, weighted liner sum, Gaussian Distribution

Unsupervised visual anomaly detection is promising for industry, due to the requirement of training only on normal images. To apply unsupervised approaches in a practical scene, we address covariate shifts by environmental changes such as lighting variations and equipment deterioration. Since embedded machines used in manufacturing processes have limited resources, the machines cannot store many images for retraining. Thus, retraining on a large dataset is impractical. In this paper, we propose an adaptive incremental learning method that avoids retraining the large dataset. The proposed model deals with the mean and covariance matrix. Several sets of mean and covariance matrix are combined by weighted linear sum. Each set is trained on a small dataset stored in the embedded machine. As a result, the proposed method achieves superior AUROC even when only a smaller dataset is trained.