A Dual-Decoder Variational Auto-Encoder for Anomaly Detection

Anomaly detection aims to identify patterns and events that deviate from the norm. However, current methods struggle to achieve high detection accuracy due to data complexity, i.e., imbalance and particularly hidden features not directly observed in the raw data. To address this, we propose a novel...

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Vydané v:	IEEE Wireless Communications and Networking Conference : [proceedings] : WCNC s. 1 - 6
Hlavní autori:	Dinh, Phai Vu, Nguyen, Diep N., Thai, Hoang Dinh, Nguyen, Quang Uy, Bao, Son Pham, Dutkiewicz, Eryk
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 24.03.2025
Predmet:	Accuracy Analytical models Anomaly detection Benchmark testing Complexity theory Data models Decoding Feature extraction generative models neural network architecture Neural networks Stochastic processes
ISSN:	1558-2612
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