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Found 6 papers, 2 papers with code
Active anomaly detection based on deep one-class classification
In an active learning framework, a model queries samples to be labeled by experts and re-trains the model with the labeled data samples.
An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination
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.
Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics
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.
Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination
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.
Camera-Tracklet-Aware Contrastive Learning for Unsupervised Vehicle Re-Identification
However, this achievement requires large-scale and well-annotated datasets.
Measuring Human Adaptation to AI in Decision Making: Application to Evaluate Changes after AlphaGo
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
