The recent rapid development of deep learning has laid a milestone in industrial image
anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning …

Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

We propose a simple and application-friendly network (called SimpleNet) for detecting and
localizing anomalies. SimpleNet consists of four components:(1) a pre-trained Feature …

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 …

Visual anomaly detection is commonly used in industrial quality inspection. In this paper, we
present a new dataset as well as a new self-supervised learning method for ImageNet pre …

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 …

During industrial processing, unforeseen defects may arise in products due to uncontrollable
factors. Although unsupervised methods have been successful in defect localization, the …

Industrial anomaly detection (IAD) has garnered significant attention and experienced rapid
development. However the recent development of IAD approach has encountered certain …

Anomaly detection and localization are widely used in industrial manufacturing for its
efficiency and effectiveness. Anomalies are rare and hard to collect and supervised models …

Unsupervised anomaly localization and detection is crucial for industrial manufacturing
processes due to the lack of anomalous samples. Recent unsupervised advances on …