[2A1] [General Session] 2. Machine Learning

[2A1-03] Anomaly Manufacturing Product Detection using Unregularized Anomaly Score on Deep Generative Models

Ryosuke Tachibana¹, Takashi Matsubara¹, 〇Kuniaki Uehara¹ (1. Graduate School of System Informatics, Kobe Universiry)

Keywords:Anomaly Detection, Deep Learning, Pattern Recognition

One of the most common needs in manufacturing plants is rejecting products not coincident with the standards as anomalies. Manufacturing companies usually employ numerous inspectors for anomaly detection and it takes a high cost. Accurate and automatic anomaly detection reduces inspection cost and improves product reliability. In unsupervised anomaly detection, a probabilistic model detects test samples with lower likelihoods as anomalies. Recently, a probabilistic model called deep generative model (DGM) has been proposed for end-to-end modeling of natural images and already achieved a certain success. However, anomaly detection of machine components with complicated structures is still challenging because they produce a wide variety of the normal image patches with lower likelihoods. For overcoming this difficulty, we propose unregularized score for the DGM. As its name implies, the unregularized score is the anomaly score of the DGM without the regularization terms. The unregularized score is robust to the inherent complexity of a sample and has a smaller risk of rejecting a sample appearing less frequently but being coincident with the standards. Experimental results of anomaly detection on the real manufacturing datasets show better performance of the unregularized score compared to existing approaches.