An Anomaly Detection Method for Hyperspectral Imagery in Kernel Feature Space Based on Robust Analysis

2013 ◽  
Vol 42 (8) ◽  
pp. 883-890
Author(s):  
赵锐 ZHAO Ruia ◽  
杜博 DU Bob ◽  
张良培 ZHANG Liangpeia
Keyword(s):  
Anomaly Detection ◽  
Detection Method ◽  
Hyperspectral Imagery ◽  
Feature Space ◽  
Robust Analysis

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

Proposal of the Anomaly Detection Method of Live-alone by LOF using Multiple Sensors

2015 ◽  
Vol 135 (12) ◽  
pp. 749-755
Author(s):  
Taiyo Matsumura ◽  
Ippei Kamihira ◽  
Katsuma Ito ◽  
Takashi Ono
Keyword(s):  
Anomaly Detection ◽  
Detection Method ◽  
Multiple Sensors

Adaptive anomaly detection method of Web-based attacks

2013 ◽  
Vol 32 (7) ◽  
pp. 2003-2006
Author(s):  
Kai WEN ◽  
Fan GUO ◽  
Min YU
Keyword(s):  
Anomaly Detection ◽  
Detection Method ◽  
Web Based

WSAD: An Unsupervised Web Session Anomaly Detection Method

2020 ◽  
Author(s):  
Yizhen Sun ◽  
Yiman Xie ◽  
Weiping Wang ◽  
Shigeng Zhang ◽  
Jun Gao ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Detection Method

scholarly journals Ensemble-Based Out-of-Distribution Detection

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 567
Author(s):  
Donghun Yang ◽  
Kien Mai Mai Ngoc ◽  
Iksoo Shin ◽  
Kyong-Ha Lee ◽  
Myunggwon Hwang
Keyword(s):  
Detection Method ◽  
State Of The Art ◽  
Metric Learning ◽  
Feature Space ◽  
Confidence Score ◽  
Distance Metric Learning ◽  
Current State ◽  
Overall Performance ◽  
Deep Learning Model

To design an efficient deep learning model that can be used in the real-world, it is important to detect out-of-distribution (OOD) data well. Various studies have been conducted to solve the OOD problem. The current state-of-the-art approach uses a confidence score based on the Mahalanobis distance in a feature space. Although it outperformed the previous approaches, the results were sensitive to the quality of the trained model and the dataset complexity. Herein, we propose a novel OOD detection method that can train more efficient feature space for OOD detection. The proposed method uses an ensemble of the features trained using the softmax-based classifier and the network based on distance metric learning (DML). Through the complementary interaction of these two networks, the trained feature space has a more clumped distribution and can fit well on the Gaussian distribution by class. Therefore, OOD data can be efficiently detected by setting a threshold in the trained feature space. To evaluate the proposed method, we applied our method to various combinations of image datasets. The results show that the overall performance of the proposed approach is superior to those of other methods, including the state-of-the-art approach, on any combination of datasets.

Sign in / Sign up

Export Citation Format

Share Document