Unsupervised Anomaly Detection with Specified Settings -- 20% anomaly
4 papers with code • 5 benchmarks • 5 datasets
Most implemented papers
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection
In this paper, we present a Deep Autoencoding Gaussian Mixture Model (DAGMM) for unsupervised anomaly detection.
Robust Subspace Recovery Layer for Unsupervised Anomaly Detection
The encoder maps the data into a latent space, from which the RSR layer extracts the subspace.
Shell Theory: A Statistical Model of Reality
The foundational assumption of machine learning is that the data under consideration is separable into classes; while intuitively reasonable, separability constraints have proven remarkably difficult to formulate mathematically.
Locally varying distance transform for unsupervised visual anomaly detection
Unsupervised anomaly detection on image data is notoriously unstable.