125 citations · 201 across the 14 of their papers we have counts for
4 papers · 1 filter
Adversarial Denoising Diffusion Model for Unsupervised Anomaly Detection
Jongmin Yu, Hyeontaek Oh, Jinhong Yang
In this paper, we propose the Adversarial Denoising Diffusion Model (ADDM). The ADDM is based on the Denoising Diffusion Probabilistic Model (DDPM) but complementarily trained by a…
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…