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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/jiiahaoxu/masa

공식 코드

Identify Backdoored Model in Federated Learning via Individual Unlearning

https://github.com/jim8220/ws-auroc

공식 코드

Performance Metric for Multiple Anomaly Score Distributions with Discrete Severity Levels

https://github.com/jin-pu/drone-anomaly

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Anomaly Detection in Aerial Videos with Transformers

https://github.com/jindongli-ai/cvtgad

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CVTGAD: Simplified Transformer with Cross-View Attention for Unsupervised Graph-level Anomaly Detection

https://github.com/jing-ds/hnn_gad

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Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message Passing and Hyperbolic Neural Networks

https://github.com/jinghanSunn/CoE-DG

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Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation

https://github.com/jingkang50/oodsurvey

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Generalized Out-of-Distribution Detection: A Survey

https://github.com/jingkang50/openood

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Generalized Out-of-Distribution Detection: A Survey

https://github.com/jingtao-li-cver/asd

공식 코드

Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors

https://github.com/jingtao-li-cver/uniadrs

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Learning a Cross-modality Anomaly Detector for Remote Sensing Imagery

https://github.com/jingyuanchan/CVPR_AICity_2021_Anomaly_Detection

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Dual-Modality Vehicle Anomaly Detection via Bilateral Trajectory Tracing

https://github.com/jingzbu/ROCHM

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Statistical Anomaly Detection via Composite Hypothesis Testing for Markov Models

https://github.com/jirifilip/pyARC

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QCBA: Improving Rule Classifiers Learned from Quantitative Data by Recovering Information Lost by Discretisation

https://github.com/jnhwkim/orthoad

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Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation

https://github.com/joaggi/incremental-anomaly-detection-using-quantum-measurements

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InQMAD: Incremental Quantum Measurement Anomaly Detection

https://github.com/joaocarv/invariant-anomaly-detection

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Invariant Anomaly Detection under Distribution Shifts: A Causal Perspective

https://github.com/jonghyeonk/OnlineAnomalyDetection_Extended

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Online anomaly detection using statistical leverage for streaming business process events

https://github.com/joos2010kj/clip-tsa

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CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detection

https://github.com/jpcbertoldo/aupimo

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AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerance

https://github.com/jqtangust/IUF

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An Incremental Unified Framework for Small Defect Inspection

https://github.com/jqtangust/hawk

공식 코드

Hawk: Learning to Understand Open-World Video Anomalies

https://github.com/jukworks/swat-seq2seq

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Anomaly Detection for Industrial Control Systems Using Sequence-to-Sequence Neural Networks

https://github.com/juliawolleb/anomaly_berdiff

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Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detection

https://github.com/jun-jie-huang/lofi

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Demystifying and Extracting Fault-indicating Information from Logs for Failure Diagnosis

https://github.com/jun-sato/anatpaste

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Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest Radiographs

https://github.com/jungyg/tailedcore

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TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection

https://github.com/junha-kim/Learning-to-Adapt-to-Unseen-Abnormal-Activities

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Learning to Adapt to Unseen Abnormal Activities under Weak Supervision

https://github.com/jupall/lcpr-data

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Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates

https://github.com/jupall/swfilter

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Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates

https://github.com/justin900429/nsf-hdr-challenge

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Anomaly Detection for Hybrid Butterfly Subspecies via Probability Filtering

https://github.com/jweihe/ADA-GAD

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ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection

https://github.com/jx-zhong-for-academic-purpose/GCN-Anomaly-Detection

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Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection

https://github.com/jzc777/mapl

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MAPL: Memory Augmentation and Pseudo-Labeling for Semi-Supervised Anomaly Detection

https://github.com/kaichen-z/rad

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RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic Observations

https://github.com/kaist-dmlab/arcus

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Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream

https://github.com/kaize0409/Meta-GDN_AnomalyDetection

공식 코드

Few-shot Network Anomaly Detection via Cross-network Meta-learning

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