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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/lab-v2/error-detection-and-correction

공식 코드

Rule-Based Error Detection and Correction to Operationalize Movement Trajectory Classification

https://github.com/labsaint/tsi-gan

공식 코드

TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional Cycle-Consistent Generative Adversarial Networks

https://github.com/lancek23/trajectory-kinematics

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Kinematic Detection of Anomalies in Human Trajectory Data

https://github.com/laurab222/tsad

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Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series

https://github.com/laurarkart/Physical-Anomalous-Trajectory-or-Motion-PHANTOM-Dataset

공식 코드

Approaches Toward Physical and General Video Anomaly Detection

https://github.com/layaars/unsupervised-anomaly-detection-with-a-gan-augmented-autoencoder

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on the effectiveness of generative adversarial network on anomaly detection

https://github.com/lcs-crr/ma-vae

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MA-VAE: Multi-head Attention-based Variational Autoencoder Approach for Anomaly Detection in Multivariate Time-series Applied to Automotive Endurance Powertrain Testing

https://github.com/lcs-crr/path

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PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time Series

https://github.com/lcs-crr/tevae

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TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly Detection in Variable-state Multivariate Time-series Data

https://github.com/leeway-95/aligning-tsd-with-llm

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Prioritizing Alignment Paradigms over Task-Specific Model Customization in Time-Series LLMs

https://github.com/let-it-care/t-rep

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T-Rep: Representation Learning for Time Series using Time-Embeddings

https://github.com/lewandofskee/DiAD

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DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection

https://github.com/lewandofskee/mambaad

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MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

https://github.com/lfoppiano/supercon2

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Semi-automatic staging area for high-quality structured data extraction from scientific literature

https://github.com/lht1949/AnomalyDetection

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Anomaly Detection via Graphical Lasso

https://github.com/li1neo/lan

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LAN: Learning Adaptive Neighbors for Real-Time Insider Threat Detection

https://github.com/lifelonglab/lifelong-anomaly-detection-scenarios

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Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights

https://github.com/lilygeorgescu/AED

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A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video

https://github.com/lilygeorgescu/mae-medical-anomaly-detection

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Masked Autoencoders for Unsupervised Anomaly Detection in Medical Images

https://github.com/lilygeorgescu/ubnormal

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UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection

https://github.com/lingxiaoshawn/glam

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Graph Anomaly Detection with Unsupervised GNNs

https://github.com/linkedin/alertiger

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AlerTiger: Deep Learning for AI Model Health Monitoring at LinkedIn

https://github.com/linkedin/greykite

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Greykite: Deploying Flexible Forecasting at Scale at LinkedIn

https://github.com/liurui39660/Isconna

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Isconna: Streaming Anomaly Detection with Frequency and Patterns

https://github.com/liurui39660/Isconna.Python

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Isconna: Streaming Anomaly Detection with Frequency and Patterns

https://github.com/liushuoer/flight2vec

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Effective and Efficient Representation Learning for Flight Trajectories

https://github.com/liutongkun/comad

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Component-aware anomaly detection framework for adjustable and logical industrial visual inspection

https://github.com/liutongkun/edgrec

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Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detection

https://github.com/liutongkun/fair

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FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection

https://github.com/liwk16/StackVAE

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StackVAE-G: An efficient and interpretable model for time series anomaly detection

https://github.com/liznerski/eoe

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Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images

https://github.com/liznerski/fcdd

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Explainable Deep One-Class Classification

https://github.com/ljj-cyber/topogdn

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Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysis

https://github.com/llcresearch/tsde

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Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask

https://github.com/lliang97/spacecraft-anonamly-detection

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Spacecraft Anomaly Detection with Attention Temporal Convolution Network

https://github.com/llien30/point_cloud_anomaly_detection

공식 코드

Toward Unsupervised 3D Point Cloud Anomaly Detection using Variational Autoencoder

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