Outlier Detection
194 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
MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation Learning
fdudsde/magic
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Data provenance analysis on provenance graphs has emerged as a common approach in APT detection.
Data Cleaning and Machine Learning: A Systematic Literature Review
First, it aims to summarize the latest approaches for data cleaning for ML and ML for data cleaning.
Distribution and volume based scoring for Isolation Forests
We make two contributions to the Isolation Forest method for anomaly and outlier detection.
Outlier-Insensitive Kalman Filtering: Theory and Applications
State estimation of dynamical systems from noisy observations is a fundamental task in many applications.
Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs
In this work, we propose a novel unsupervised Skin Lesion sEgmentation framework based on structural entropy and isolation forest outlier Detection, namely SLED.
kTrans: Knowledge-Aware Transformer for Binary Code Embedding
Learner0x5a/kTrans-release
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By feeding explicit knowledge as additional inputs to the Transformer, and fusing implicit knowledge with a novel pre-training task, kTrans provides a new perspective to incorporating domain knowledge into a Transformer framework.
Quantile-based Maximum Likelihood Training for Outlier Detection
Previous attempts to address this challenge involved training image classifiers through contrastive learning using actual outlier data or synthesizing outliers for self-supervised learning.
Learning on Graphs with Out-of-Distribution Nodes
Graph Neural Networks (GNNs) are state-of-the-art models for performing prediction tasks on graphs.
Uncertainty Quantification for Image-based Traffic Prediction across Cities
alextimans/traffic4cast-uncertainty
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We compare two epistemic and two aleatoric UQ methods on both temporal and spatio-temporal transfer tasks, and find that meaningful uncertainty estimates can be recovered.
Image Outlier Detection Without Training using RANSAC
Furthermore, we show that RANSAC-NN can enhance the robustness of existing methods by incorporating our algorithm as part of the data preparation process.
