• DeepWalk

  • Referenced in 71 articles [sw39604]
  • applications such as network classification, and anomaly detection...
  • Dlib-ml

  • Referenced in 12 articles [sw14413]
  • based methods for classification, regression, clustering, anomaly detection, and feature ranking. To enable easy...
  • OddBall

  • Referenced in 11 articles [sw40655]
  • these rules for anomaly detection; (b) we carefully choose features, and design oddball, so that...
  • EFDR

  • Referenced in 11 articles [sw11106]
  • Rate (EFDR) is a tool to detect anomalies in an image. The image is first...
  • MILA

  • Referenced in 10 articles [sw02726]
  • MILA is flexible and efficient in detecting anomalies and novel patterns...
  • GANomaly

  • Referenced in 5 articles [sw41240]
  • GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training. Anomaly detection is a classical problem ... challenging problem is that of detecting the unknown/unseen anomaly case that takes us instead into ... paradigm. We introduce such a novel anomaly detection model, by using a conditional generative adversarial ... outlier from that distribution - an anomaly. Experimentation over several benchmark datasets, from varying domains, shows...
  • Colibri

  • Referenced in 9 articles [sw12043]
  • finding patterns (such as communities) and detecting anomalies. Additionally, it is desirable to track...
  • GigaTensor

  • Referenced in 6 articles [sw23741]
  • applications including clustering, trend detection, and anomaly detection. However, current tensor decomposition algorithms...
  • FRaC

  • Referenced in 3 articles [sw15627]
  • FRaC (Feature Regression and Classification) Anomaly Detection Algorithm. FRaC is a new general approach ... anomaly detection problem; that is, the task of identifying instances that come from a different ... distribution than the majority (unsupervised anomaly detection) or a set of verified “normal” data (semi ... supervised anomaly detection). Traditional approaches typically compare the position of a new data point...
  • Meta-AAD

  • Referenced in 2 articles [sw41888]
  • Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning. High false-positive rate ... long-standing challenge for anomaly detection algorithms, especially in high-stake applications. To identify ... list of anomalies identified by an anomaly detection system. This verification procedure generates informative labels ... this work, we propose Active Anomaly Detection with Meta-Policy (Meta-AAD), a novel framework...
  • DynGEM

  • Referenced in 4 articles [sw40461]
  • visualization, graph reconstruction, link prediction and anomaly detection (on both synthetic and real datasets). Experimental...
  • LeSiNN

  • Referenced in 2 articles [sw20748]
  • LeSiNN: Detecting anomalies by identifying least similar nearest neighbours. We introduce the concept of Least ... Neighbours (LeSiNN) and use LeSiNN to detect anomalies directly. Although there is an existing method ... state-of-the-art anomaly detectors in terms of detection accuracy and runtime...
  • adamethods

  • Referenced in 2 articles [sw38640]
  • package adamethods: Archetypoid Algorithms and Anomaly Detection. Collection of several algorithms to obtain archetypoids with ... these algorithms also allow to detect anomalies (outliers). Please see Vinue and Epifanio...
  • scanstatistics

  • Referenced in 3 articles [sw22348]
  • package scanstatistics: Space-Time Anomaly Detection using Scan Statistics. Detection of anomalous space-time clusters ... data streams, scanning for clusters with ongoing anomalies. Hypothesis testing is made possible by Monte...
  • Anomalib

  • Referenced in 1 article [sw41238]
  • Anomalib: A Deep Learning Library for Anomaly Detection. This paper introduces anomalib, a novel library ... unsupervised anomaly detection and localization. With reproducibility and modularity in mind, this open-source library ... tools to design custom anomaly detection algorithms via a plug-and-play approach. Anomalib comprises ... state-of-the-art anomaly detection algorithms that achieve top performance on the benchmarks...
  • GLAD

  • Referenced in 1 article [sw40658]
  • GLAD: Group Anomaly Detection in Social Media Analysis- Extended Abstract. Traditional anomaly detection on social ... detect group anomalies. Existing group anomaly detection approaches rely on the assumption that the groups ... hierarchical Bayes model: Group Latent Anomaly Detection (GLAD) model. GLAD takes both pair-wise ... input, automatically infers the groups and detects group anomalies simultaneously. To account for the dynamic...
  • RADE

  • Referenced in 1 article [sw41100]
  • RADE: resource-efficient supervised anomaly detection using decision tree-based ensemble methods. The capability ... perform anomaly detection in a resource-constrained setting, such as an edge device ... datasets often results in current anomaly detection methods being too resource consuming, and in particular ... present RADE – a new resource-efficient anomaly detection framework that augments standard decision-tree based...
  • Banpei

  • Referenced in 1 article [sw38171]
  • Python package of the anomaly detection. Anomaly detection is a technique used to identify unusual ... conform to expected behavior. Anomaly detection library based on singular spectrum transformation...
  • CFLOW-AD

  • Referenced in 1 article [sw41241]
  • CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows. Unsupervised anomaly ... detection with localization has many practical applications when labeling is infeasible and, moreover, when anomaly ... conditional normalizing flow framework adopted for anomaly detection with localization. In particular, CFLOW-AD consists...
  • stray

  • Referenced in 1 article [sw41746]
  • stray: Anomaly Detection in High Dimensional and Temporal Data. This is a modification of ’HDoutliers ... powerful unsupervised algorithm for detecting anomalies in high-dimensional data, with a strong theoretical foundation ... Hyndman and Smith-Miles (2019) for detecting anomalies in high-dimensional data that addresses these...