125 citations · 198 across the 10 of their papers we have counts for
5 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…
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…
Boosting Mapping Functionality of Neural Networks via Latent Feature Generation based on Reversible Learning
Jongmin Yu
This paper addresses a boosting method for mapping functionality of neural networks in visual recognition such as image classification and face recognition. We present reversible l…
Boosting Network Weight Separability via Feed-Backward Reconstruction
Jongmin Yu, Hyeontaek Oh
This paper proposes a new evaluation metric and boosting method for weight separability in neural network design. In contrast to general visual recognition methods designed to enco…