A Generative Adversarial Active Learning Method for Effective Outlier Detection-Reference-Cited by-同舟云学术

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A Generative Adversarial Active Learning Method for Effective Outlier Detection

Published:2022-10 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)
language:
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Author:

Bah Mohamed Jaward¹,Zhang Ji²,Yu Ting¹,Xia Feng³,Li Zhao⁴,Zhou Shuigeng⁵,Wang Hongzhi⁶

Affiliation:

1. Big Data Intelligence Research Center, Zhejiang Lab,Hangzhou,China

2. University of Southern Queensland, School of Sciences,Toowoomba,Australia

3. Institute of Innovation, Science and Sustainability, Federation University Australia,Ballarat,Australia

4. Zhejiang University,China

5. Fudan University, School of Computer Science,Shanghai,China

6. Harbin Institute of Technology, Massive Data Computing Lab,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10097829/10097885/10098011.pdf?arnumber=10098011

Reference30 articles.

1. Rcc-dual-gan: An efficient approach for outlier detection with few identified anomalies;li;ArXiv Preprint,2020

2. Ganomaly:Semi-supervised anomaly detection via adversarial training;akcay;Asian Conference on Computer Vision,0

3. Combining Over-Sampling and Under-Sampling Techniques for Imbalance Dataset

4. Active Learning by Querying Informative and Representative Examples

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Detecting Outliers in Non-IID Data: A Systematic Literature Review;IEEE Access;2023

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