Results 31 to 40 of about 4,738,087 (364)
VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization [PDF]
International Symposium on Industrial Electronics, 2021 We present a transformer-based image anomaly detection and localization network. Our proposed model is a combination of a reconstruction-based approach and patch embedding.
P. Mishra +4 more
semanticscholar +1 more source
Anomaly Detection As-a-Service [PDF]
2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), 2019 Paper accepted at the Intl. Workshop on Governing Adaptive and Unplanned Systems of Systems (GAUSS)
Mobilio, M +4 more
openaire +3 more sources
Rethinking Graph Neural Networks for Anomaly Detection [PDF]
International Conference on Machine Learning, 2022 Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As one of the key components for GNN design is to select a tailored spectral filter, we take the first step towards analyzing anomalies via the lens of the graph spectrum.
Jianheng Tang +3 more
semanticscholar +1 more source
Detecting network performance anomalies with contextual anomaly detection [PDF]
2017 IEEE International Workshop on Measurement and Networking (M&N), 2017 Network performance anomalies can be defined as abnormal and significant variations in a network's traffic levels. Being able to detect anomalies is critical for both network operators and end users. However, the accurate detection without raising false alarms can become a challenging task when there is high variance in the traffic.
Dimopoulos, Giorgos +3 more
openaire +3 more sources
Subspace-Based Anomaly Detection for Large-Scale Campus Network Traffic
Journal of Applied Mathematics, 2023 With the continuous development of information technology and the continuous progress of traffic bandwidth, the types and methods of network attacks have become more complex, posing a great threat to the large-scale campus network environment.
Xiaofeng Zhao, Qiubing Wu
doaj +1 more source
Saliencycut: Augmenting Plausible Anomalies for Anomaly Detection
Pattern Recognition, 2023 Anomaly detection under open-set scenario is a challenging task that requires learning discriminative fine-grained features to detect anomalies that were even unseen during training. As a cheap yet effective approach, data augmentation has been widely used to create pseudo anomalies for better training of such models.
Jianan Ye +5 more
openaire +2 more sources
Improvement in detection of presence in forbidden locations in video anomaly using optical flow map [PDF]
هوش محاسباتی در مهندسی برق, 2023 Anomaly detection has been in researchers’ scope of study for a long time. The wide variety of anomaly detection use cases ranges from quality control in production lines to providing security in public places.
Mohammad Rahimpour +3 more
doaj +1 more source
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection [PDF]
Knowledge Discovery and Data Mining, 2023 Time series anomaly detection is critical for a wide range of applications. It aims to identify deviant samples from the normal sample distribution in time series. The most fundamental challenge for this task is to learn a representation map that enables
Yiyuan Yang +4 more
semanticscholar +1 more source
Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022 Abstract Positron emission tomography with x‐ray computed tomography (PET/CT) is increasingly being utilized for radiation treatment planning (RTP). Accurate delivery of RT therefore depends on quality PET/CT data. This study covers quality control (QC) procedures required for PET/CT for diagnostic imaging and incremental QC required for RTP.
Ran Klein +7 more
wiley +1 more source
Multimodal Industrial Anomaly Detection via Hybrid Fusion [PDF]
Computer Vision and Pattern Recognition, 2023 2D-based Industrial Anomaly Detection has been widely discussed, however, multimodal industrial anomaly detection based on 3D point clouds and RGB images still has many untouched fields.
Yue Wang +5 more
semanticscholar +1 more source