Outlier Detection

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

Benchmarks

Libraries

Datasets

Subtasks

Most implemented papers

Towards Total Recall in Industrial Anomaly Detection

Content

LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection

LSTM Fully Convolutional Networks for Time Series Classification

Deep Semi-Supervised Anomaly Detection

Deep Sets

Adversarially Learned One-Class Classifier for Novelty Detection

PyOD: A Python Toolbox for Scalable Outlier Detection

ADBench: Anomaly Detection Benchmark

Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection