Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly

4 papers with code • 5 benchmarks • 5 datasets

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Most implemented papers

Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection

danieltan07/dagmm ICLR 2018

In this paper, we present a Deep Autoencoding Gaussian Mixture Model (DAGMM) for unsupervised anomaly detection.

Robust Subspace Recovery Layer for Unsupervised Anomaly Detection

dmzou/RSRAE ICLR 2020

The encoder maps the data into a latent space, from which the RSR layer extracts the subspace.

Shell Theory: A Statistical Model of Reality

wen-yan-lin/shell-theory IEEE Transactions on Pattern Analysis and Machine Intelligence 2021

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.