29 citations · 68 across the 7 of their papers we have counts for
4 papers · 1 filter
Active anomaly detection based on deep one-class classification
Minkyung Kim, Junsik Kim, Jongmin Yu +1
Active learning has been utilized as an efficient tool in building anomaly detection models by leveraging expert feedback. In an active learning framework, a model queries samples…
An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination
Minkyung Kim, Jongmin Yu, Junsik Kim +2
Most deep anomaly detection models are based on learning normality from datasets due to the difficulty of defining abnormality by its diverse and inconsistent nature. Therefore, it…
Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics
Minkyung Kim, Junsik Kim, Jongmin Yu +1
One-class classification has been a prevailing method in building deep anomaly detection models under the assumption that a dataset consisting of normal samples is available. In pr…
Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination
Jongmin Yu, Hyeontaek Oh, Minkyung Kim +1
In this paper, we propose Normality-Calibrated Autoencoder (NCAE), which can boost anomaly detection performance on the contaminated datasets without any prior information or expli…