Outlier Detection
229 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.
Benchmarks
These leaderboards are used to track progress in Outlier Detection
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Libraries
Use these libraries to find Outlier Detection models and implementations
Fashion-MNIST
MVTecAD
ImageNet-O
pathbased
Ecoli
SKAB
Most implemented papers
Towards Total Recall in Industrial Anomaly Detection
amazon-research/patchcore-inspection
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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
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
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Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine.
Deep Sets
aai-institute/pyDVL
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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.
Deep Semi-Supervised Anomaly Detection
lukasruff/Deep-SAD-PyTorch
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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
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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.
ADBench: Anomaly Detection Benchmark
minqi824/adbench
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Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of anomalies, and (iii) noisy and corrupted data?
PyOD: A Python Toolbox for Scalable Outlier Detection
yzhao062/pyod
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PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data.
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
hendrycks/ss-ood
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NeurIPS 2019
Self-supervision provides effective representations for downstream tasks without requiring labels.
Probabilistic Autoencoder
VMBoehm/PAE
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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.
