Unsupervised Anomaly Detection
166 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.
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Latest papers
MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
Recent advancements in anomaly detection have seen the efficacy of CNN- and transformer-based approaches.
Fusing Dictionary Learning and Support Vector Machines for Unsupervised Anomaly Detection
We introduce a new anomaly detection model that unifies the OC-SVM and DL residual functions into a single composite objective, subsequently solved through K-SVD-type iterative algorithms.
SoftPatch: Unsupervised Anomaly Detection with Noisy Data
Noise discriminators are utilized to generate outlier scores for patch-level noise elimination before coreset construction.
Diffusion Models with Ensembled Structure-Based Anomaly Scoring for Unsupervised Anomaly Detection
We demonstrate that this ensembling strategy can enhance the performance of DMs and mitigate the sensitivity to different kernel sizes across varying pathologies, highlighting its promise for brain MRI anomaly detection.
Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection
Finally, we report the results of popular IAD methods on the Real-IAD dataset, providing a highly challenging benchmark to promote the development of the IAD field.
From Chaos to Clarity: Time Series Anomaly Detection in Astronomical Observations
However, existing time series anomaly detection methods fall short in tackling the unique characteristics of astronomical observations where each star is inherently independent but interfered by random concurrent noise, resulting in a high rate of false alarms.
Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images
To this end, we propose a Simple Space-Aware Memory Matrix for In-painting and Detecting anomalies from radiography images (abbreviated as SimSID).
Diffusion Models with Implicit Guidance for Medical Anomaly Detection
Diffusion models have advanced unsupervised anomaly detection by improving the transformation of pathological images into pseudo-healthy equivalents.
A SAM-guided Two-stream Lightweight Model for Anomaly Detection
In this paper, considering these two critical factors, we propose a SAM-guided Two-stream Lightweight Model for unsupervised anomaly detection (STLM) that not only aligns with the two practical application requirements but also harnesses the robust generalization capabilities of SAM.
Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET
Over the past years, pseudo-healthy reconstruction for unsupervised anomaly detection has gained in popularity.