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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/aebtehaj/ftcencoder

공식 코드

An Autoencoder Architecture for L-band Passive Microwave Retrieval of Landscape Freeze-Thaw Cycle

https://github.com/aenglebert/multimodal_bone

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Self-supervised vision-langage alignment of deep learning representations for bone X-rays analysis

https://github.com/aenglebert/ortho_vlp_eval

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Self-supervised vision-langage alignment of deep learning representations for bone X-rays analysis

https://github.com/aeon-toolkit/aeon

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aeon: a Python toolkit for learning from time series

https://github.com/againerju/maad_highway

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Anomaly Detection in Multi-Agent Trajectories for Automated Driving

https://github.com/agao8/STEAD

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STEAD: Spatio-Temporal Efficient Anomaly Detection for Time and Compute Sensitive Applications

https://github.com/agoodge/ares

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ARES: Locally Adaptive Reconstruction-based Anomaly Scoring

https://github.com/agoodge/lunar

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LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks

https://github.com/agrija9/Wind-Turbine-Anomaly-Detection-VRAE

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Anomaly Detection of Wind Turbine Time Series using Variational Recurrent Autoencoders

https://github.com/agustdd/floss

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Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization Approach

https://github.com/ahachang/defend

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Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement

https://github.com/ahachang/mitigate

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Multitask Active Learning for Graph Anomaly Detection

https://github.com/ahaque/twitch-troll-detection

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Twitch Plays Pokemon, Machine Learns Twitch: Unsupervised Context-Aware Anomaly Detection for Identifying Trolls in Streaming Data

https://github.com/ahmadrezania/fdm-bench

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FDM-Bench: A Comprehensive Benchmark for Evaluating Large Language Models in Additive Manufacturing Tasks

https://github.com/ahsanmah/sade

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Localizing Anomalies via Multiscale Score Matching Analysis

https://github.com/ahstat/affiliation-metrics-py

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Local Evaluation of Time Series Anomaly Detection Algorithms

https://github.com/ahxmeds/igconda-pet

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IgCONDA-PET: Weakly-Supervised PET Anomaly Detection using Implicitly-Guided Attention-Conditional Counterfactual Diffusion Modeling -- a Multi-Center, Multi-Cancer, and Multi-Tracer Study

https://github.com/ai-for-edu/time-series-analysis-for-education

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Time Series Analysis for Education: Methods, Applications, and Future Directions

https://github.com/aicenter/GenerativeAD.jl

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Comparison of Anomaly Detectors: Context Matters

https://github.com/aimagelab/Consistency-based-Self-supervised-Learning-for-Temporal-Anomaly-Localization

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Consistency-based Self-supervised Learning for Temporal Anomaly Localization

https://github.com/aimagelab/novelty-detection

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Latent Space Autoregression for Novelty Detection

https://github.com/airi-institute/sensorscan

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SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical Processes

https://github.com/ais-clemson/visiongpt

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VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual Navigation

https://github.com/ait-aecid/anomaly-detection-log-datasets

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A Critical Review of Common Log Data Sets Used for Evaluation of Sequence-based Anomaly Detection Techniques

https://github.com/ait-aecid/llm-log-parsing

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System Log Parsing with Large Language Models: A Review

https://github.com/aitorzan3/awesome-self-supervised-time-series-anomaly-detection

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A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challenges

https://github.com/ajaychawda58/uadad

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Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings

https://github.com/ajayjain/lmconv

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Locally Masked Convolution for Autoregressive Models

https://github.com/ajoshiusc/qrvae

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Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detection

https://github.com/akanshaaga/reg_appnp

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Addressing the Impact of Localized Training Data in Graph Neural Networks

https://github.com/akanuasiegbu/bipoco

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BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly Detection

https://github.com/akanuasiegbu/leveraging-trajectory-prediction-for-pedestrian-video-anomaly-detection

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Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection

https://github.com/akdd11/dt-radio-environment-novel-approach

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Digital Twin of the Radio Environment: A Novel Approach for Anomaly Detection in Wireless Networks

https://github.com/alain-ryser/tvae

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Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models

https://github.com/alan-turing-institute/anomaly_with_experts

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Anomaly detection with superexperts under delayed feedback

https://github.com/alanjohnvarghese/TransformerG2G

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TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers

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