Unsupervised Anomaly Detection
164 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.
Benchmarks
These leaderboards are used to track progress in Unsupervised Anomaly Detection
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Libraries
Use these libraries to find Unsupervised Anomaly Detection models and implementationsDatasets
MNIST
Fashion-MNIST
STL-10
Caltech-101
MVTecAD
Reuters-21578
MVTec LOCO AD
KolektorSDD
Subtasks
Latest papers with no code
Explainable Online Unsupervised Anomaly Detection for Cyber-Physical Systems via Causal Discovery from Time Series
Online unsupervised detection of anomalies is crucial to guarantee the correct operation of cyber-physical systems and the safety of humans interacting with them.
Reap the Wild Wind: Detecting Media Storms in Large-Scale News Corpora
Both the method and dataset offer the basis for comprehensive empirical research into the concept of media storms, including characterizing them and predicting their outbursts and durations, in mainstream media or social media platforms.
Multi-Image Visual Question Answering for Unsupervised Anomaly Detection
To the best of our knowledge, we are the first to leverage a language model for unsupervised anomaly detection, for which we construct a dataset with different questions and answers.
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.
Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment
We propose Class-Agnostic Distribution Alignment (CADA) to align the mismatched score distribution of each implicit class without knowing class information, which enables unified anomaly detection for all classes and samples.
Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detection
As diffusion-based methods require a lot of GPU memory and have long sampling times, we present a novel and fast unsupervised anomaly detection approach based on latent Bernoulli diffusion models.
Anomaly Detection Based on Isolation Mechanisms: A Survey
Anomaly detection is a longstanding and active research area that has many applications in domains such as finance, security, and manufacturing.
Objective and Interpretable Breast Cosmesis Evaluation with Attention Guided Denoising Diffusion Anomaly Detection Model
As advancements in the field of breast cancer treatment continue to progress, the assessment of post-surgical cosmetic outcomes has gained increasing significance due to its substantial impact on patients' quality of life.
Dual-Student Knowledge Distillation Networks for Unsupervised Anomaly Detection
In terms of classification, we obtain pixel-wise anomaly segmentation maps by measuring the discrepancy between the output feature maps of the teacher and student networks, from which an anomaly score is computed for sample-wise determination.
UP-CrackNet: Unsupervised Pixel-Wise Road Crack Detection via Adversarial Image Restoration
Subsequently, a generative adversarial network is trained to restore the corrupted regions by leveraging the semantic context learned from surrounding uncorrupted regions.
