Unsupervised anomaly detection (UAD) aims to recognize anomalous images based on the training set that contains only normal images. In medical image analysis, UAD benefits from leveraging the easily ...

Abnormal defect pattern detection plays a key role in preventing yield loss excursion events for the semiconductor manufacturing. We present a method for detecting and segmenting abnormal wafer map ...

A professionally curated list of awesome resources (paper, code, data, etc.) on Deep Graph Anomaly Detection (DGAD), which is the first work to comprehensively and systematically summarize the recent ...

Circumventing the need for large datasets with labelled defects, unsupervised anomaly detection methods focus on learning the high-level representations of non-defective, normal data to identify ...

The performance of anomaly detection has been improved by the use of encoder–decoder architecture. To further improve the reconstruction effect, the encoder–decoder architecture incorporates an ...

Brief: Oak Ridge National Laboratory (ORNL) researchers have developed a novel self-supervised anomaly detection approach based on normalizing flows with an active learning scheme for determining ...