Results 261 to 270 of about 5,452,269 (307)

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

Information Processing in Medical Imaging, 2017
Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection.
T. Schlegl, Philipp Seeböck, S. Waldstein, U. Schmidt-Erfurth, G. Langs   +4 more
semanticscholar   +1 more source

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

Asian Conference on Computer Vision, 2018
Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal). While this
S. Akçay, Amir Atapour-Abarghouei, T. Breckon   +2 more
semanticscholar   +1 more source

MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

Neural Information Processing Systems
Recent advancements in anomaly detection have seen the efficacy of CNN- and transformer-based approaches. However, CNNs struggle with long-range dependencies, while transformers are burdened by quadratic computational complexity. Mamba-based models, with
Haoyang He, Yuhu Bai, Jiangning Zhang, Qingdong He, Hongxu Chen, Zhenye Gan, Chengjie Wang, Xiangtai Li, Guanzhong Tian, Lei Xie   +9 more
semanticscholar   +1 more source

Program Anomaly Detection

Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 2016
This tutorial will present an overview of program anomaly detection, which analyzes normal program behaviors and discovers aberrant executions caused by attacks, misconfigurations, program bugs, and unusual usage patterns. It was first introduced as an analogy between intrusion detection for programs and the immune mechanism in biology. Advanced models
Xiaokui Shu, Danfeng Yao
openaire   +1 more source

SoftPatch: Unsupervised Anomaly Detection with Noisy Data

Neural Information Processing Systems
Although mainstream unsupervised anomaly detection (AD) algorithms perform well in academic datasets, their performance is limited in practical application due to the ideal experimental setting of clean training data.
Xi Jiang, Jianlin Liu, Jinbao Wang, Qiang Nie, WU Kai, Y. Liu, Chengjie Wang, Feng Zheng   +7 more
semanticscholar   +1 more source

Anomaly detection

2022
Patrick Schneider, Fatos Xhafa
openaire   +2 more sources

A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization

European Conference on Computer Vision
Anomaly synthesis strategies can effectively enhance unsupervised anomaly detection. However, existing strategies have limitations in the coverage and controllability of anomaly synthesis, particularly for weak defects that are very similar to normal ...
Qiyu Chen, Huiyuan Luo, Chengkan Lv, Zhengtao Zhang   +3 more
semanticscholar   +1 more source

VANET Network Traffic Anomaly Detection Using GRU-Based Deep Learning Model

IEEE transactions on consumer electronics
The rise of Vehicular Ad-hoc Networks (VANETs) has led to the growing significance in intelligent transportation systems. This research suggests a deep learning model for anomaly detection based on GRU over VANET network traffic to address this challenge.
Ghayth AlMahadin, Yassine Aoudni, Mohammad Shabaz, Anurag Vijay Agrawal, Ghazaala Yasmin, E. Alomari, Hamza Mohammed Ridha Al-Khafaji, Debabrata Dansana, R. R. Maaliw   +8 more
semanticscholar   +1 more source

Anomaly Detection

2023
Juan J. Cuadrado-Gallego, Yuri Demchenko
openaire   +1 more source

Anomaly Detection in Hyperspectral Images Using Adaptive Graph Frequency Location

IEEE Transactions on Neural Networks and Learning Systems
Graph theory-based techniques have recently been adopted for anomaly detection in hyperspectral images (HSIs). However, these methods rely excessively on the relational structure within the constructed graphs and tend to downplay the importance of ...
Bing Tu, Xianchang Yang, Baoliang He, Yunyun Chen, Jun Li, Antonio Plaza   +5 more
semanticscholar   +1 more source