Unsupervised Anomaly Detection
169 papers with code • 15 benchmarks • 23 datasets
The objective of Unsupervised Anomaly Detection is to detect previously unseen rare objects or events without any prior knowledge about these. The only information available is that the percentage of anomalies in the dataset is small, usually less than 1%. Since anomalies are rare and unknown to the user at training time, anomaly detection in most cases boils down to the problem of modelling the normal data distribution and defining a measurement in this space in order to classify samples as anomalous or normal. In high-dimensional data such as images, distances in the original space quickly lose descriptive power (curse of dimensionality) and a mapping to some more suitable space is required.
Libraries
Use these libraries to find Unsupervised Anomaly Detection models and implementationsDatasets
Subtasks
Most implemented papers
Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation
We present the efficiency of semi-orthogonal embedding for unsupervised anomaly segmentation.
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows
Our approach results in a computationally and memory-efficient model: CFLOW-AD is faster and smaller by a factor of 10x than prior state-of-the-art with the same input setting.
DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection
Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance.
Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection
We leverage on outputs of several anomaly detectors as a representation that already captures the basic notion of anomalousness and estimate the contamination using a specific mixture formulation.
How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?
When sufficient labeled data are available, classical criteria based on Receiver Operating Characteristic (ROC) or Precision-Recall (PR) curves can be used to compare the performance of un-supervised anomaly detection algorithms.
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.
Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection
By contrast, we introduce an unsupervised anomaly detection model, trained only on the normal (non-anomalous, plentiful) samples in order to learn the normality distribution of the domain and hence detect abnormality based on deviation from this model.
adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection
We assume that both the anomalous and the normal prior distribution are Gaussian and have overlaps in the latent space.
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.
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure
a statistical population may play a crucial role in this regard, anomalies corresponding to observations with 'small' depth.