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 with no code
Outlier detection by ensembling uncertainty with negative objectness
We therefore reconsider direct prediction of K+1 logits that correspond to K groundtruth classes and one outlier class.
A Comprehensive System for Secondary Structure Analysis of Protein Models
In protein structure analysis, the accurate characterization of secondary structure elements is crucial for understanding protein function and dynamics.
Can we predict QPP? An approach based on multivariate outliers
Query performance prediction (QPP) aims to forecast the effectiveness of a search engine across a range of queries and documents.
Outlier Ranking in Large-Scale Public Health Streams
Disease control experts inspect public health data streams daily for outliers worth investigating, like those corresponding to data quality issues or disease outbreaks.
User-Assisted Networked Sensing in OFDM Cellular Network with Erroneous Anchor Position Information
However, in practice, the number of BSs possessing LOS paths to a target can be small, leading to marginal networked sensing gain.
Outlier detection using flexible categorisation and interrogative agendas
Building on formal concept analysis (FCA), the starting point of the present work is that different ways to categorize a given set of objects exist, which depend on the choice of the sets of features used to classify them, and different such sets of features may yield better or worse categorizations, relative to the task at hand.
A Hybrid Intelligent Framework for Maximising SAG Mill Throughput: An Integration of Expert Knowledge, Machine Learning and Evolutionary Algorithms for Parameter Optimisation
This study introduces a hybrid intelligent framework leveraging expert knowledge, machine learning techniques, and evolutionary algorithms to address this research need.
Ocean Data Quality Assessment through Outlier Detection-enhanced Active Learning
Ocean and climate research benefits from global ocean observation initiatives such as Argo, GLOSS, and EMSO.
RANRAC: Robust Neural Scene Representations via Random Ray Consensus
We demonstrate the compatibility and potential of our solution for both photo-realistic robust multi-view reconstruction from real-world images based on neural radiance fields and for single-shot reconstruction based on light-field networks.
HLoOP -- Hyperbolic 2-space Local Outlier Probabilities
Within a Euclidean space, well-known techniques for local outlier detection are based on the Local Outlier Factor (LOF) and its variant, the LoOP (Local Outlier Probability), which incorporates probabilistic concepts to model the outlier level of a data vector.
