Outlier Detection
191 papers with code • 11 benchmarks • 11 datasets
Outlier Detection is a task of identifying a subset of a given data set which are considered anomalous in that they are unusual from other instances. It is one of the core data mining tasks and is central to many applications. In the security field, it can be used to identify potentially threatening users, in the manufacturing field it can be used to identify parts that are likely to fail.
Libraries
Use these libraries to find Outlier Detection models and implementationsMost implemented papers
An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers
This article starts with a categorization of the various methods.
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
Self-supervision provides effective representations for downstream tasks without requiring labels.
AdaLAM: Revisiting Handcrafted Outlier Detection
Local feature matching is a critical component of many computer vision pipelines, including among others Structure-from-Motion, SLAM, and Visual Localization.
COPOD: Copula-Based Outlier Detection
In this work, we make three key contributions, 1) propose a novel, parameter-free outlier detection algorithm with both great performance and interpretability, 2) perform extensive experiments on 30 benchmark datasets to show that COPOD outperforms in most cases and is also one of the fastest algorithms, and 3) release an easy-to-use Python implementation for reproducibility.
SSD: A Unified Framework for Self-Supervised Outlier Detection
We demonstrate that SSD outperforms most existing detectors based on unlabeled data by a large margin.
Generalized Out-of-Distribution Detection: A Survey
In this survey, we first present a unified framework called generalized OOD detection, which encompasses the five aforementioned problems, i. e., AD, ND, OSR, OOD detection, and OD.
Geometry- and Accuracy-Preserving Random Forest Proximities
Random forests are considered one of the best out-of-the-box classification and regression algorithms due to their high level of predictive performance with relatively little tuning.
Zero-Shot Learning Through Cross-Modal Transfer
This work introduces a model that can recognize objects in images even if no training data is available for the objects.
Generative Adversarial Active Learning for Unsupervised Outlier Detection
In this paper, we approach outlier detection as a binary-classification issue by sampling potential outliers from a uniform reference distribution.
Explaining Anomalies Detected by Autoencoders Using SHAP
Anomaly detection algorithms are often thought to be limited because they don't facilitate the process of validating results performed by domain experts.