Cited 0 times in Scipus Cited Count

Skip-GANomaly++: Skip Connections and Residual Blocks for Anomaly Detection (Student Abstract)

Authors
Park, JY | Hong, JR | Kim, MH | Kim, TJ
Citation
Proceedings of the AAAI Conference on Artificial Intelligence, 38(21). : 23615-23617, 2024
Journal Title
Proceedings of the AAAI Conference on Artificial Intelligence
ISSN
2159-53992374-3468
Abstract
Anomaly detection is a critical task across various domains. Fundamentally, anomaly detection models offer methods to identify unusual patterns that do not align with expected behaviors. Notably, in the medical field, detecting anomalies in medical imagery or biometrics can facilitate early diagnosis of diseases. Consequently, we propose the Skip-GANomaly++ model, an enhanced and more efficient version of the conventional anomaly detection models. The proposed model's performance was evaluated through comparative experiments. Experimental results demonstrated superior performance across most classes compared to the previous models.
Keywords

DOI
10.1609/aaai.v38i21.30496
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Neurology
Ajou Authors
김, 태준
Files in This Item:
There are no files associated with this item.
Export

qrcode

해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse