Outlier Detection

126 papers with code • 10 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

Most implemented papers

Towards Total Recall in Industrial Anomaly Detection

amazon-research/patchcore-inspection 15 Jun 2021

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.

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.

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.

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.

Probabilistic Auto-Encoder

VMBoehm/PAE Under review 2020

We introduce the probabilistic auto-encoder (PAE), a generative model with a lower dimensional latent space that is based on an auto-encoder which is interpreted probabilistically after training using a normalizing flow.

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.