Outlier Detection
192 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
Latest papers
Interactive Continual Learning: Fast and Slow Thinking
biqing-qi/interactive-continual-learning-fast-and-slow-thinking
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Drawing on Complementary Learning System theory, this paper presents a novel Interactive Continual Learning (ICL) framework, enabled by collaborative interactions among models of various sizes.
Can Tree Based Approaches Surpass Deep Learning in Anomaly Detection? A Benchmarking Study
The paper contributes significantly by conducting an unbiased comparison of various anomaly detection algorithms, spanning classical machine learning including various tree-based approaches to deep learning and outlier detection methods.
Efficient Generation of Hidden Outliers for Improved Outlier Detection
The only existing method accounting for this property falls short in efficiency and effectiveness.
Dimensionality-Aware Outlier Detection: Theoretical and Experimental Analysis
We present a nonparametric method for outlier detection that takes full account of local variations in intrinsic dimensionality within the dataset.
Unsupervised Outlier Detection using Random Subspace and Subsampling Ensembles of Dirichlet Process Mixtures
Probabilistic mixture models are acknowledged as a valuable tool for unsupervised outlier detection owing to their interpretability and intuitive grounding in statistical principles.
Data Augmentation for Supervised Graph Outlier Detection with Latent Diffusion Models
kayzliu/godm
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One of the fundamental challenges confronting supervised graph outlier detection algorithms is the prevalent issue of class imbalance, where the scarcity of outlier instances compared to normal instances often results in suboptimal performance.
Enhancing Traffic Flow Prediction using Outlier-Weighted AutoEncoders: Handling Real-Time Changes
himanshudce/owam
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Moreover, Given the dynamic nature of traffic, the need for real-time traffic modeling also becomes crucial to ensure accurate and up-to-date traffic predictions.
Meta-survey on outlier and anomaly detection
From this comprehensive collection, a subset of 56 papers that claim to be general surveys on outlier detection is selected using a snowball search technique to enhance field coverage.
SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised Learning
yue-fan/ssb
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ICCV 2023
In experiments, SSB greatly improves both inlier classification and outlier detection performance, outperforming existing methods by a large margin.
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?
We began by empirically evaluating 6 modern machine learning-based outlier detection algorithms on the task of identifying irregular data in 838 datasets from 7 real-world MCRCTs with a total of 77, 001 patients from over 44 countries.
