no code implementations • 18 Sep 2023 • Minkyung Kim, Junsik Kim, Jongmin Yu, Jun Kyun Choi
In an active learning framework, a model queries samples to be labeled by experts and re-trains the model with the labeled data samples.
no code implementations • 18 Sep 2023 • Minkyung Kim, Jongmin Yu, Junsik Kim, Tae-Hyun Oh, Jun Kyun Choi
Therefore, it has been a common practice to learn normality under the assumption that anomalous data are absent in a training dataset, which we call normality assumption.
no code implementations • 13 Feb 2023 • Minkyung Kim, Junsik Kim, Jongmin Yu, Jun Kyun Choi
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.
1 code implementation • 28 Oct 2021 • Jongmin Yu, Hyeontaek Oh, Minkyung Kim, Junsik Kim
In this paper, we propose Normality-Calibrated Autoencoder (NCAE), which can boost anomaly detection performance on the contaminated datasets without any prior information or explicit abnormal samples in the training phase.
1 code implementation • 14 Sep 2021 • Jongmin Yu, Junsik Kim, Minkyung Kim, Hyeontaek Oh
However, this achievement requires large-scale and well-annotated datasets.
no code implementations • 30 Dec 2020 • Minkyu Shin, Minkyung Kim, Jin Kim
Across a growing number of domains, human experts are expected to learn from and adapt to AI with superior decision making abilities.
Decision Making Human-Computer Interaction General Economics Economics Applications