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Found 4 papers, 2 papers with code
Reimagining Anomalies: What If Anomalies Were Normal?
Deep learning-based methods have achieved a breakthrough in image anomaly detection, but their complexity introduces a considerable challenge to understanding why an instance is predicted to be anomalous.
Deep Anomaly Detection on Tennessee Eastman Process Data
Our extensive study will facilitate choosing appropriate anomaly detection methods in industrial applications.
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
We find that standard classifiers and semi-supervised one-class methods trained to discern between normal samples and relatively few random natural images are able to outperform the current state of the art on an established AD benchmark with ImageNet.
Ranked #1 on
Anomaly Detection
on One-class CIFAR-10
(using extra training data)
Explainable Deep One-Class Classification
Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped away.
Ranked #5 on
Anomaly Detection
on One-class ImageNet-30
(using extra training data)
