[19](1,2)Xu, Hongzuo et al. "Deep Isolation Forest for Anomaly Detection". TKDE. 2023. [20]Xu, Hongzuo et al. "Fascinating supervisory signals and where to find them: deep anomaly detection with scale learning". ICML. 2023.
Due to the difficulty and cost of collecting large-scale labeled anomaly data, it is important to havedata-efficient learning of normality/abnormality (Challenge #3). wo major challenges are how to learn expressive normality/abnormality representations with a small amount of labeled anomaly data and...
This post summaries a comprehensive survey paper on deep learning for anomaly detection —“Deep Learning for Anomaly Detection: A Review” [1], discussing challenges, methods and opportunities in this…
This enables anomaly detectors to generalize and detect unseen anomalies. In extensive experiments on natural language processing and small- and large-scale vision tasks, we find that Outlier Exposure significantly improves detection performance. We also observe that cutting-edge generative models trained ...
Deep learningIntelligent monitoringAnomaly detectionAlexNet networkIn order to improve the real-time efficiency of expressway operation monitoring and management, the anomaly detection in intelligent monitoring network of expressway based on edge computing and deep learning is studied. The video data ...
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data 来自 ACM 喜欢 0 阅读量: 104 作者:G Pang,AJVD Hengel,C Shen,L Cao 摘要: We consider the problem of anomaly detection with a small set of partially labeled anomaly examples and a large-scale ...
2021. ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 3122–3126. [19] Ming Jin, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, and Shirui Pan. 2021. Multi-Scale ...
In alignment with the aforementioned goals we formulate the research questions of this survey as follows: The remainder of this paper is structured as follows. Section 2 first explains the terms deep learning, log data, and anomaly detection, and then provides an overview of common challenges. ...
This paper proposes an accurate anomaly detection technique that built upon deep learning approach. We proposed a Combined Deep Q-Learning (CDQL) algorithm for anomaly detection. Priory, optimal features are selected by using Spider Monkey Optimizer (SMO). With the optimal features, CDQL detects ...
We hypothesized that, in discrimination between benign and malignant parotid gland tumors, high diagnostic accuracy could be obtained with a small amount of imbalanced data when anomaly detection (AD) was combined with deep leaning (DL) model and the L2-