GAN-based anomaly detection: A review
X **a, 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 …
Simplenet: A simple network for image anomaly detection and localization
Z Liu, Y Zhou, Y Xu, Z Wang - Proceedings of the IEEE/CVF …, 2023 - openaccess.thecvf.com
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 …
localizing anomalies. SimpleNet consists of four components:(1) a pre-trained Feature …
Weakly-supervised video anomaly detection with robust temporal feature magnitude learning
Anomaly detection with weakly supervised video-level labels is typically formulated as a
multiple instance learning (MIL) problem, in which we aim to identify snippets containing …
multiple instance learning (MIL) problem, in which we aim to identify snippets containing …
Deep industrial image anomaly detection: A survey
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 …
anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning …
Catching both gray and black swans: Open-set supervised anomaly detection
Despite most existing anomaly detection studies assume the availability of normal training
samples only, a few labeled anomaly examples are often available in many real-world …
samples only, a few labeled anomaly examples are often available in many real-world …
Omni-frequency channel-selection representations for unsupervised anomaly detection
Density-based and classification-based methods have ruled unsupervised anomaly
detection in recent years, while reconstruction-based methods are rarely mentioned for the …
detection in recent years, while reconstruction-based methods are rarely mentioned for the …
Anomaly detection for industrial quality assurance: A comparative evaluation of unsupervised deep learning models
J Zipfel, F Verworner, M Fischer, U Wieland… - Computers & Industrial …, 2023 - Elsevier
Across many industries, visual quality assurance has transitioned from a manual, labor-
intensive, and error-prone task to a fully automated and precise assessment of industrial …
intensive, and error-prone task to a fully automated and precise assessment of industrial …
[BOOK][B] Deep learning for medical image analysis
Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for
academic and industry researchers and graduate students taking courses on machine …
academic and industry researchers and graduate students taking courses on machine …
Toward generalist anomaly detection via in-context residual learning with few-shot sample prompts
This paper explores the problem of Generalist Anomaly Detection (GAD) aiming to train one
single detection model that can generalize to detect anomalies in diverse datasets from …
single detection model that can generalize to detect anomalies in diverse datasets from …