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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/felime/feature-autoencoder

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Unsupervised Anomaly Localization with Structural Feature-Autoencoders

https://github.com/felixdjc/nlgad

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Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning

https://github.com/fengzicai/awesomewmad

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A Survey of World Models for Autonomous Driving

https://github.com/fengzicai/wmad-benchmarks

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A Survey of World Models for Autonomous Driving

https://github.com/fesvhtr/cuva

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Uncovering What, Why and How: A Comprehensive Benchmark for Causation Understanding of Video Anomaly

https://github.com/ffyyytt/coool_2025

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Addressing Out-of-Label Hazard Detection in Dashcam Videos: Insights from the COOOL Challenge

https://github.com/fingertap/fird.cython

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Modeling Heterogeneous Statistical Patterns in High-dimensional Data by Adversarial Distributions: An Unsupervised Generative Framework

https://github.com/finnbehrendt/conditioned-diffusion-models-uad

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Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs

https://github.com/finnbehrendt/ensembled-ssim-for-unsupervised-anomaly-detection

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Diffusion Models with Ensembled Structure-Based Anomaly Scoring for Unsupervised Anomaly Detection

https://github.com/finnbehrendt/patched-diffusion-models-uad

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Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI

https://github.com/fjchange/MIST_VAD

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MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection

https://github.com/flixpar/med-ts-llm

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MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysis

https://github.com/flowfrontiers/cyberml-completevsfirstn

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Early-Stage Anomaly Detection: A Study of Model Performance on Complete vs. Partial Flows

https://github.com/flowfrontiers/cyberml-dataquality

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Evaluating ML-Based Anomaly Detection Across Datasets of Varied Integrity: A Case Study

https://github.com/fly-orange/treemil

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TreeMIL: A Multi-instance Learning Framework for Time Series Anomaly Detection with Inexact Supervision

https://github.com/flyinghu123/cpr

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Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval

https://github.com/fortunato-all/LJH_DiffGAD

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DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector

https://github.com/fraspass/meg

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Mutually exciting point process graphs for modelling dynamic networks

https://github.com/ftheberge/graph-partition-and-measures

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Ensemble Clustering for Graphs: Comparisons and Applications

https://github.com/fudanyliu/gvaed

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Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models

https://github.com/funz-0/one-for-more

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One-for-More: Continual Diffusion Model for Anomaly Detection

https://github.com/funz-0/promptad

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PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection

https://github.com/furkanulger/Anomaly-detection-for-solder

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Anomaly Detection for Solder Joints Using $β$-VAE

https://github.com/fverdoja/LAD-Laplacian-Anomaly-Detector

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Graph Laplacian for Image Anomaly Detection

https://github.com/fvmassoli/mocca-anomaly-detection

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MOCCA: Multi-Layer One-Class ClassificAtion for Anomaly Detection

https://github.com/fzi-forschungszentrum-informatik/anovox

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AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving

https://github.com/gaoangw/hscl

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Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection

https://github.com/gaobb/AdaptCLIP

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AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection

https://github.com/gaobb/anogen

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Few-Shot Anomaly-Driven Generation for Anomaly Classification and Segmentation

https://github.com/gaobb/onenip

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Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt

https://github.com/gaochangwu/fmf-benchmark

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Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and Benchmark

https://github.com/gchers/exact-cp-optimization

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Exact Optimization of Conformal Predictors via Incremental and Decremental Learning

https://github.com/gdahia/meta_occ

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Meta Learning for Few-Shot One-class Classification

https://github.com/gdwang08/jigsaw-vad

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Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles

https://github.com/geoffroyo/onlineem

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Towards frugal unsupervised detection of subtle abnormalities in medical imaging

https://github.com/ghksdl6025/streaming_anomaly_detect

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The Analysis of Online Event Streams: Predicting the Next Activity for Anomaly Detection

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