Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies-Reference-Cited by-同舟云学术

Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies

Author:

Zaheer Muhammad Zaigham¹^ORCID,Lee Jin-Ha²,Mahmood Arif³^ORCID,Astrid Marcella⁴^ORCID,Lee Seung-Ik⁴^ORCID

Affiliation:

1. Department of Computer Vision, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

2. Korea Telecom (KT), Infra DX R&D Center, Daejeon, South Korea

3. Department of Computer Science, Information Technology University, Lahore, Pakistan

4. Department of Artificial Intelligence, University of Science and Technology, Daejeon, South Korea

Funder

Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT)

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Graphics and Computer-Aided Design,Software

Link

Reference84 articles.

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