Search Results for author:
Found 6 papers, 3 papers with code
Deep Anomaly Detection on Tennessee Eastman Process Data
Our extensive study will facilitate choosing appropriate anomaly detection methods in industrial applications.
Ordinal Regression for Difficulty Estimation of StepMania Levels
As is common in popular games, there is a large number of community-designed levels.
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)
A systematic approach to random data augmentation on graph neural networks
On the theoretical side, among other results, we formally prove that under natural conditions all instantiations of our framework are universal.
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)
Rethinking Assumptions in Deep Anomaly Detection
Though anomaly detection (AD) can be viewed as a classification problem (nominal vs. anomalous) it is usually treated in an unsupervised manner since one typically does not have access to, or it is infeasible to utilize, a dataset that sufficiently characterizes what it means to be "anomalous."
