Outlier Detection

155 papers with code • 11 benchmarks • 10 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


Use these libraries to find Outlier Detection models and implementations
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Most implemented papers

Towards Total Recall in Industrial Anomaly Detection

amazon-research/patchcore-inspection CVPR 2022

Being able to spot defective parts is a critical component in large-scale industrial manufacturing.

LSTM Fully Convolutional Networks for Time Series Classification

houshd/LSTM-FCN 8 Sep 2017

We propose the augmentation of fully convolutional networks with long short term memory recurrent neural network (LSTM RNN) sub-modules for time series classification.

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

chickenbestlover/RNN-Time-series-Anomaly-Detection 1 Jul 2016

Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine.

Deep Semi-Supervised Anomaly Detection

lukasruff/Deep-SAD-PyTorch ICLR 2020

Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets.

Adversarially Learned One-Class Classifier for Novelty Detection

khalooei/ALOCC-CVPR2018 CVPR 2018

Our architecture is composed of two deep networks, each of which trained by competing with each other while collaborating to understand the underlying concept in the target class, and then classify the testing samples.

PyOD: A Python Toolbox for Scalable Outlier Detection

yzhao062/pyod 6 Jan 2019

PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data.

Probabilistic Autoencoder

VMBoehm/PAE Under review 2020

The PAE is fast and easy to train and achieves small reconstruction errors, high sample quality, and good performance in downstream tasks.

Deep Sets

lwtnn/lwtnn NeurIPS 2017

Our main theorem characterizes the permutation invariant functions and provides a family of functions to which any permutation invariant objective function must belong.

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

xuhongzuo/DeepOD 13 Jun 2018

However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i. e., outliers).