Applications of generative adversarial networks in anomaly detection: a systematic literature review
Anomaly detection has become an indispensable tool for modern society, applied in a wide
range of applications, from detecting fraudulent transactions to malignant brain tumors. Over …
range of applications, from detecting fraudulent transactions to malignant brain tumors. Over …
A review of neural networks for anomaly detection
JE de Albuquerque Filho, LCP Brandão… - IEEE …, 2022 - ieeexplore.ieee.org
Anomaly detection is a critical issue across several academic fields and real-world
applications. Artificial neural networks have been proposed to detect anomalies from …
applications. Artificial neural networks have been proposed to detect anomalies from …
Robust unsupervised network intrusion detection with self-supervised masked context reconstruction
Modern network intrusion detection systems always utilize deep learning to improve their
intelligence and feature learning abilities. To overcome the difficulties of accessing a large …
intelligence and feature learning abilities. To overcome the difficulties of accessing a large …
Anomaly detection methods based on GAN: a survey
Anomaly detection (AD) is an enduring topic, and it has been used in various fields, such as
fraud detection, industrial fault diagnosis, and medical image diagnosis. With the continuous …
fraud detection, industrial fault diagnosis, and medical image diagnosis. With the continuous …
Fabric defect segmentation system based on a lightweight GAN for industrial Internet of Things
B Li, Y Zou, R Zhu, W Yao, J Wang… - … and Mobile Computing, 2022 - Wiley Online Library
Machine vision systems based on deep learning play an important role in the industrial
Internet of things (IIoT) and Industry 4.0 applications, especially for product quality …
Internet of things (IIoT) and Industry 4.0 applications, especially for product quality …
Dissolving is amplifying: Towards fine-grained anomaly detection
Medical imaging often contains critical fine-grained features, such as tumors or
hemorrhages, which are crucial for diagnosis yet potentially too subtle for detection with …
hemorrhages, which are crucial for diagnosis yet potentially too subtle for detection with …
Metabolic anomaly appearance aware U-Net for automatic lymphoma segmentation in whole-body PET/CT scans
T Shi, H Jiang, M Wang, Z Diao… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Positron emission tomography-computed tomography (PET/CT) is an essential imaging
instrument for lymphoma diagnosis and prognosis. PET/CT image based automatic …
instrument for lymphoma diagnosis and prognosis. PET/CT image based automatic …
Enhancing Portfolio Optimization with Transformer-GAN Integration: A Novel Approach in the Black-Litterman Framework
E Zhu, J Yen - arxiv preprint arxiv:2404.02029, 2024 - arxiv.org
This study presents an innovative approach to portfolio optimization by integrating
Transformer models with Generative Adversarial Networks (GANs) within the Black …
Transformer models with Generative Adversarial Networks (GANs) within the Black …
A critical study on the recent deep learning based semi-supervised video anomaly detection methods
Video anomaly detection (VAD) is currently a trending research area within computer vision,
given that anomalies form a key detection objective in surveillance systems, often requiring …
given that anomalies form a key detection objective in surveillance systems, often requiring …
[HTML][HTML] A self-learning algorithm for identifying the leverage points in soil data using quantile regression forests
Some unusual combinations of predictor values in multivariate regression often influence
tampering with the output, and filtering those observations becomes the trickiest and most …
tampering with the output, and filtering those observations becomes the trickiest and most …