GAN-based anomaly detection: A review
X Xia, X Pan, N Li, X He, L Ma, X Zhang, N Ding - Neurocomputing, 2022 - Elsevier
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 …
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …
Deep learning for unsupervised anomaly localization in industrial images: A survey
X Tao, X Gong, X Zhang, S Yan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
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 …
supervised learning methods. However, in real industrial scenarios, the scarcity of defect …
Anomaly detection requires better representations
Anomaly detection seeks to identify unusual phenomena, a central task in science and
industry. The task is inherently unsupervised as anomalies are unexpected and unknown …
industry. The task is inherently unsupervised as anomalies are unexpected and unknown …
Self-supervised anomaly detection in computer vision and beyond: A survey and outlook
Anomaly detection (AD) plays a crucial role in various domains, including cybersecurity,
finance, and healthcare, by identifying patterns or events that deviate from normal behavior …
finance, and healthcare, by identifying patterns or events that deviate from normal behavior …
Self-supervised anomaly detection: A survey and outlook
Over the past few years, anomaly detection, a subfield of machine learning that is mainly
concerned with the detection of rare events, witnessed an immense improvement following …
concerned with the detection of rare events, witnessed an immense improvement following …
Exposing outlier exposure: What can be learned from few, one, and zero outlier images
Due to the intractability of characterizing everything that looks unlike the normal data,
anomaly detection (AD) is traditionally treated as an unsupervised problem utilizing only …
anomaly detection (AD) is traditionally treated as an unsupervised problem utilizing only …
Back to the feature: classical 3d features are (almost) all you need for 3d anomaly detection
Despite significant advances in image anomaly detection and segmentation, few methods
use 3D information. We utilize a recently introduced 3D anomaly detection dataset to …
use 3D information. We utilize a recently introduced 3D anomaly detection dataset to …
Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detection
T Liu, B Li, Z Zhao, X Du, B Jiang, L Geng - arXiv preprint arXiv …, 2022 - arxiv.org
Reconstruction-based methods are widely explored in industrial visual anomaly detection.
Such methods commonly require the model to well reconstruct the normal patterns but fail in …
Such methods commonly require the model to well reconstruct the normal patterns but fail in …
A survey on unsupervised anomaly detection algorithms for industrial images
In line with the development of Industry 4.0, surface defect detection/anomaly detection
becomes a topical subject in the industry field. Improving efficiency as well as saving labor …
becomes a topical subject in the industry field. Improving efficiency as well as saving labor …
A survey of methods for automated quality control based on images
J Diers, C Pigorsch - International Journal of Computer Vision, 2023 - Springer
The role of quality control based on images is important in industrial production.
Nevertheless, this problem has not been addressed in computer vision for a long time. In …
Nevertheless, this problem has not been addressed in computer vision for a long time. In …