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Quantitative benchmarking of anomaly detection methods in digital pathology images [PDF]

Machine Learning: Health
Anomaly detection has been widely studied in the context of industrial defect inspection, with numerous methods developed to tackle a range of challenges.
Can Cui, Xindong Zheng, Ruining Deng, Quan Liu, Tianyuan Yao, Keith T Wilson, Lori A Coburn, Bennett A Landman, Haichun Yang, Yaohong Wang, Yuankai Huo   +10 more
doaj   +2 more sources

Anomaly Detection Using Graph Anomaly Rules

Big Data Mining and Analytics
Anomaly detection in attribute networks is utilized to discover patterns of individuals or groups that deviate from the majority, and is widely used in areas such as e-commerce and social media.
Bowen Dong, Wenjun Wang, Lifei Sun, Xueli Liu, Zhenzhen Mai   +4 more
doaj   +2 more sources

Research Progress of Video Anomaly Detection Technology [PDF]

Jisuanji kexue yu tansuo, 2022
Video anomaly detection refers to the detection and identification of events that deviate from normal behavior, which has a wide range of applications in surveillance video.
WU Kaijun, HUANG Tao, WANG Dicong, BAI Chenshuai, TAO Xiaomiao
doaj   +1 more source

SA-PatchCore: Anomaly Detection in Dataset With Co-Occurrence Relationships Using Self-Attention

IEEE Access, 2023
Various unsupervised anomaly detection methods using deep learning have recently been proposed, and the accuracy of the anomaly detection technique for local anomalies has been improved.
Kengo Ishida, Yuki Takena, Yoshiki Nota, Rinpei Mochizuki, Itaru Matsumura, Gosuke Ohashi   +5 more
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, Yijie Hu, Xi Yang, Qiu-Feng Wang, Chao Huang, Kaizhu Huang   +5 more
openaire   +2 more sources

Using a nested anomaly detection machine learning algorithm to study the neutral triple gauge couplings at an e+e− collider

Nuclear Physics B, 2022
Anomaly detection algorithms have been proved to be useful in the search of new physics beyond the Standard Model. However, a prerequisite for using an anomaly detection algorithm is that the signal to be sought is indeed anomalous.
Ji-Chong Yang, Yu-Chen Guo, Li-Hua Cai
doaj   +1 more source

Predictive anomaly detection for marine diesel engine based on echo state network and autoencoder

Energy Reports, 2022
Marine diesel engine with high thermal efficiency and good economy has become the main power of ships. Anomaly detection is an important method to improve the operation reliability of marine diesel engine.
Chong Qu, Zhiguo Zhou, Zhiwen Liu, Shuli Jia   +3 more
doaj   +1 more source

Collaborative Detection Technology of SDN Abnormal Traffic Based on Federated Learning [PDF]

Jisuanji gongcheng, 2023
Anomaly detection is an effective method for discovering potential security threats.However, current anomaly detection methods used in the Software-Defined Network(SDN) exhibit commom problems such as weak adaptability, poor coordination.This study ...
CHEN Hexiong, LUO Yuwei, WEI Yunkai, GUO Wei, HANG Feilu, HE Yingjun, YANG Ning
doaj   +1 more source

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

Detecting Semantic Anomalies

Proceedings of the AAAI Conference on Artificial Intelligence, 2020
We critically appraise the recent interest in out-of-distribution (OOD) detection and question the practical relevance of existing benchmarks. While the currently prevalent trend is to consider different datasets as OOD, we argue that out-distributions of practical interest are ones where the distinction is semantic in nature for a specified context ...
Ahmed, Faruk, Courville, Aaron
openaire   +3 more sources