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.

Source: Coverage-based Outlier Explanation

Libraries

Use these libraries to find Outlier Detection models and implementations
5 papers
7,889
2 papers
1,180
2 papers
766
2 papers
257
See all 6 libraries.

Most implemented papers

Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

hendrycks/ss-ood NeurIPS 2019

Self-supervision provides effective representations for downstream tasks without requiring labels.

AdaLAM: Revisiting Handcrafted Outlier Detection

cavalli1234/AdaLAM 7 Jun 2020

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

winstonll/COPOD 20 Sep 2020

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

inspire-group/SSD ICLR 2021

We demonstrate that SSD outperforms most existing detectors based on unlabeled data by a large margin.

Generalized Out-of-Distribution Detection: A Survey

jingkang50/openood 21 Oct 2021

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

kevinmoonlab/rf-gap 29 Jan 2022

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

mganjoo/zslearning NeurIPS 2013

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

leibinghe/GAAL-based-outlier-detection 28 Sep 2018

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

ronniemi/explainAnomaliesUsingSHAP 6 Mar 2019

Anomaly detection algorithms are often thought to be limited because they don't facilitate the process of validating results performed by domain experts.