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Anomaly Detection

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하위 태스크: 28
평가표: 76
보존된 평가 기록: 669

Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/psacfc/gpt4ts_adapter

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Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter View

https://github.com/pseudo-skye/triad

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Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution

https://github.com/pvitoria/SpotFlareDetection

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Automatic Flare Spot Artifact Detection and Removal in Photographs

https://github.com/pygod-team/pygod

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BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs

https://github.com/qingsongedu/Awesome-SSL4TS

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Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects

https://github.com/qingsongedu/time-series-transformers-review

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Transformers in Time Series: A Survey

https://github.com/qiqigeww/hss-iad-dataset

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HSS-IAD: A Heterogeneous Same-Sort Industrial Anomaly Detection Dataset

https://github.com/qiqigeww/visionad

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Search is All You Need for Few-shot Anomaly Detection

https://github.com/qiumiao30/slmr

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An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly Detection

https://github.com/qz-wang/act

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Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment

https://github.com/raghavchalapathy/gad

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Group Anomaly Detection using Deep Generative Models

https://github.com/raghavchalapathy/oc-nn

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Anomaly Detection using One-Class Neural Networks

https://github.com/raghavchalapathy/rcae

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Robust, Deep and Inductive Anomaly Detection

https://github.com/rahmyyy/gavd

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Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video Dataset

https://github.com/ralfoan/Detection-of-high-density-anomalies

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Algorithmic Frameworks for the Detection of High Density Anomalies

https://github.com/ralfoan/SECODA

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SECODA: Segmentation- and Combination-Based Detection of Anomalies

https://github.com/raminghorbanii/pate

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PATE: Proximity-Aware Time series anomaly Evaluation

https://github.com/raminghorbanii/restad

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RESTAD: REconstruction and Similarity based Transformer for time series Anomaly Detection

https://github.com/ramon-oliveira/deepstats

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Known Unknowns: Uncertainty Quality in Bayesian Neural Networks

https://github.com/rashidrao-pk/anomaly_detection_trust_case_study

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Can I trust my anomaly detection system? A case study based on explainable AI

https://github.com/ravih18/uad_evaluation_framework

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Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET

https://github.com/rcrupi/deepgrb

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Searching for long faint astronomical high energy transients: a data driven approach

https://github.com/rcrupiisp/dtor

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DTOR: Decision Tree Outlier Regressor to explain anomalies

https://github.com/real2fish/csl

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A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning

https://github.com/realfolkcode/graphdiffusionanomaly

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Anomaly Detection in Networks via Score-Based Generative Models

https://github.com/remorax/veealign

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VeeAlign: Multifaceted Context Representation using Dual Attention for Ontology Alignment

https://github.com/renanrojasg/adv_robust_autoencoder

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Inverting Adversarially Robust Networks for Image Synthesis

https://github.com/renjithk4/assemai

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AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelines

https://github.com/rgopikrishna-vt/caad

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Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback

https://github.com/rhermary/plume

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Removing Geometric Bias in One-Class Anomaly Detection with Adaptive Feature Perturbation

https://github.com/richardchangca/deep-contrastive-metric-learning-method-to-detect-polymicrogyria-in-pediatric-brain-mri

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A Novel Center-based Deep Contrastive Metric Learning Method for the Detection of Polymicrogyria in Pediatric Brain MRI

https://github.com/ricky-ma/ODDObjects

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ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked Objects

https://github.com/ringbdstack/sgdd

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Does Graph Distillation See Like Vision Dataset Counterpart?

https://github.com/riotu-lab/DrifNet

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DriftNet: Aggressive Driving Behavior Classification using 3D EfficientNet Architecture

https://github.com/rist-ro/argo

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Automatic Feature Extraction for Heartbeat Anomaly Detection

https://github.com/ristea/aed-mae

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Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors

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