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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/Western-OC2-Lab/OASW-Concept-Drift-Detection-and-Adaptation

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A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams

https://github.com/Whishing/K-NG

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Few-shot Online Anomaly Detection and Segmentation

https://github.com/WuQiangXDU/Peri-midFormer

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Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis

https://github.com/XiaoJian923/Divide-and-Conquer

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Divide and Conquer in Video Anomaly Detection: A Comprehensive Review and New Approach

https://github.com/XiaoJian923/Kinematics-VAD

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Human Kinematics-inspired Skeleton-based Video Anomaly Detection

https://github.com/XiaoxiaoMa-MQ/Awesome-Deep-Graph-Anomaly-Detection

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A Comprehensive Survey on Graph Anomaly Detection with Deep Learning

https://github.com/YBQian-Roy/UCF-Crime-DVS

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UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly Detection with Spiking Neural Networks

https://github.com/YangSJ2019/AHEAD

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AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approach

https://github.com/Yann-zh/gradAE

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GradStop: Exploring Training Dynamics in Unsupervised Outlier Detection through Gradient

https://github.com/YingdaXia/SynthCP

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Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation

https://github.com/YingtongDou/CARE-GNN

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Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

https://github.com/Yizhe-Liu/SplatPosePlus

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SplatPose+: Real-time Image-Based Pose-Agnostic 3D Anomaly Detection

https://github.com/YossiZhao/CwA-T

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CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality Detection

https://github.com/YoungGod/DFR

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DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation

https://github.com/Yu-Lingrui/DTAAD

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DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series Data

https://github.com/Yuanzhi-H/Real-Time-Detection-Methods

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An Evaluation of Methods for Real-Time Anomaly Detection using Force Measurements from the Turning Process

https://github.com/Yuchen413/AnomalyRuler

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Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models

https://github.com/Yui010206/MoPRL

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Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection

https://github.com/YumaKoizumi/ToyADMOS-dataset

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ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection

https://github.com/ZEKAICHEN/GTA

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Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT

https://github.com/ZKI-PH-ImageAnalysis/GANetic-Loss

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GANetic Loss for Generative Adversarial Networks with a Focus on Medical Applications

https://github.com/a-rebmann/llms4pm

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Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining Tasks

https://github.com/aaaceo890/mamba_tsad

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Joint Selective State Space Model and Detrending for Robust Time Series Anomaly Detection

https://github.com/abastola0/cpad

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Anomaly Detection in Cooperative Vehicle Perception Systems under Imperfect Communication

https://github.com/abc-125/segad

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Supervised Anomaly Detection for Complex Industrial Images

https://github.com/abc-125/viad-benchmark

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Beyond Academic Benchmarks: Critical Analysis and Best Practices for Visual Industrial Anomaly Detection

https://github.com/abedicodes/scl-dad

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Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours from Multimodal Videos

https://github.com/abhibha1807/robustifying_visual_perception

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Enhancing Visual Perception in Novel Environments via Incremental Data Augmentation Based on Style Transfer

https://github.com/abhijeetptl5/historoi

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Efficient Quality Control of Whole Slide Pathology Images with Human-in-the-loop Training

https://github.com/abhijeetptl5/oral_cyto_dataset

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A Cytology Dataset for Early Detection of Oral Squamous Cell Carcinoma

https://github.com/acoursey3/freeway-anomaly-data

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FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalous Event Detection

https://github.com/adelic99/seeker

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Sequential keypoint density estimator: an overlooked baseline of skeleton-based video anomaly detection

https://github.com/adlilog/adlilog

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Leveraging Log Instructions in Log-based Anomaly Detection

https://github.com/advanced-vision-and-learning-lab/padim-ace

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Patch distribution modeling framework adaptive cosine estimator (PaDiM-ACE) for anomaly detection and localization in synthetic aperture radar imagery

https://github.com/advanced-vision-and-learning-lab/sariad

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Benchmarking Suite for Synthetic Aperture Radar Imagery Anomaly Detection (SARIAD) Algorithms

https://github.com/ae-apt/ae-apt

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Hack Me If You Can: Aggregating AutoEncoders for Countering Persistent Access Threats Within Highly Imbalanced Data

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.