Currently, deep learning-based visual inspection has been highly successful with the help of
supervised learning methods. However, in real industrial scenarios, the scarcity of defect …

The detection of product defects is essential in quality control in manufacturing. This study
surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects …

Detecting anomalies in images is an important task, especially in real-time computer vision
applications. In this work, we focus on computational efficiency and propose a lightweight …

Abstract Knowledge distillation (KD) achieves promising results on the challenging problem
of unsupervised anomaly detection (AD). The representation discrepancy of anomalies in …

Unsupervised anomaly detection with localization has many practical applications when
labeling is infeasible and, moreover, when anomaly examples are completely missing in the …

We aim at constructing a high performance model for defect detection that detects unknown
anomalous patterns of an image without anomalous data. To this end, we propose a two …

We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect
and localize anomalies in images in a one-class learning setting. PaDiM makes use of a …

We present a transformer-based image anomaly detection and localization network. Our
proposed model is a combination of a reconstruction-based approach and patch …

Anomaly detection is a challenging task and usually formulated as an one-class learning
problem for the unexpectedness of anomalies. This paper proposes a simple yet powerful …

Unsupervised representation learning has proved to be a critical component of anomaly
detection/localization in images. The challenges to learn such a representation are two-fold …