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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/dammsi/AnomalyDINO

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AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2

https://github.com/damo-di-ml/cikm22-tfad

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TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis

https://github.com/damo-di-ml/one_fits_all

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One Fits All:Power General Time Series Analysis by Pretrained LM

https://github.com/dandolodavid/acme

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Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD

https://github.com/daniel-bogdoll/anomaly_datasets

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Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey

https://github.com/daniel-bogdoll/unknown_objects_roads

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Multimodal Detection of Unknown Objects on Roads for Autonomous Driving

https://github.com/daniellli/lion

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LiON: Learning Point-wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic Data

https://github.com/daniellli/pad

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LiON: Learning Point-wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic Data

https://github.com/daochenzha/Meta-AAD

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Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning

https://github.com/datct00/Beyond-Traditional-Approaches-Multi-Task-Network-for-Breast-Ultrasound-Diagnosis

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Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound Diagnosis

https://github.com/davegabe/Wearable-Stress-Monitor

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Seamless Monitoring of Stress Levels Leveraging a Universal Model for Time Sequences

https://github.com/david-cortes/isotree

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Revisiting randomized choices in isolation forests

https://github.com/davide-belli/context-encoding-chest-xrays

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Context Encoding Chest X-rays

https://github.com/davidumsonst/finitesampleguaranteesforquantileestimation

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Finite sample guarantees for quantile estimation: An application to detector threshold tuning

https://github.com/davnn/probabilistic-distance

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A Probabilistic Transformation of Distance-Based Outliers

https://github.com/dawnvince/easytsad

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TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models

https://github.com/dawnvince/mts_cad

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Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detection

https://github.com/ddzxlining/SOMAD

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Anomaly Detection via Self-organizing Map

https://github.com/decisionintelligence/catch

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CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching

https://github.com/decisionintelligence/tab

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TAB: Unified Benchmarking of Time Series Anomaly Detection Methods

https://github.com/deeplearning-wisc/id_label

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When and How Does In-Distribution Label Help Out-of-Distribution Detection?

https://github.com/deepskies/deepastrouda

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Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection

https://github.com/deeptsad/bdm

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Time series anomaly detection with reconstruction-based state-space models

https://github.com/deltadahl/cytodiffusion

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Deep Generative Classification of Blood Cell Morphology

https://github.com/demetrislappas/ddl

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Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection

https://github.com/demolakstate/AdeNet-Deep-Learning-Architecture

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AdeNet: Deep learning architecture that identifies damaged electrical insulators in power lines

https://github.com/denisilie94/pyod-dl

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Anomaly Detection with Selective Dictionary Learning

https://github.com/dessertlab/Failure-Dataset-OpenStack

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Enhancing the Analysis of Software Failures in Cloud Computing Systems with Deep Learning

https://github.com/devrishivermaa/capsule-commandos

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Multi-Class Abnormality Classification Task in Video Capsule Endoscopy

https://github.com/dhpr-dataset/dhpr-dataset

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Exploring the Potential of Multi-Modal AI for Driving Hazard Prediction

https://github.com/diasporeunizar/teg

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tegdet: An extensible Python Library for Anomaly Detection using Time-Evolving Graphs

https://github.com/diozaka/anomaly-eval

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Statistical Evaluation of Anomaly Detectors for Sequences

https://github.com/diptakuet/mad-en-microarchitectural-attack-detection

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MAD-EN: Microarchitectural Attack Detection through System-wide Energy Consumption

https://github.com/diulhio/pcb_anomaly

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Image-Based Detection of Modifications in Gas Pump PCBs with Deep Convolutional Autoencoders

https://github.com/dm2-nd/eland

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Action Sequence Augmentation for Early Graph-based Anomaly Detection

https://github.com/dmzou/RSRAE

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Robust Subspace Recovery Layer for Unsupervised Anomaly Detection

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