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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/nazat28/xai-based-feature-ensemble-for-enhanced-anomaly-detection-in-autonomous-driving-systems

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XAI-based Feature Ensemble for Enhanced Anomaly Detection in Autonomous Driving Systems

https://github.com/nazim1021/OOD-detection-using-OECC

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Outlier Exposure with Confidence Control for Out-of-Distribution Detection

https://github.com/ncbi-nlp/COVID-19-CT-CXR

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COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature

https://github.com/nec-research/syslrn-euromlsys22

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syslrn: Learning What to Monitor for Efficient Anomaly Detection

https://github.com/neelanjana314/nnv

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Robustness Verification of Deep Neural Networks using Star-Based Reachability Analysis with Variable-Length Time Series Input

https://github.com/neelkanwal/equipping-computational-pathology-systems-with-artifact-processing-pipeline

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Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs

https://github.com/nesg-ugr/msnm-sensor

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MSNM-Sensor: An Applied Network Monitoring Tool for Anomaly Detection in Complex Networks and Systems

https://github.com/ngoix/EMMV_benchmarks

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How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?

https://github.com/nguoikhongnao/real_oecl

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Understanding normalization in contrastive representation learning and out-of-distribution detection

https://github.com/nguyendohoangkhoiUF/Swift-Hydra

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Swift Hydra: Self-Reinforcing Generative Framework for Anomaly Detection with Multiple Mamba Models

https://github.com/niamhbelton/fewsome

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FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks

https://github.com/niamhbelton/ss-fewsome_disease_severity_knee_osteoarthritis

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An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data

https://github.com/nifangbaage/explicit-visual-prompt

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Explicit Visual Prompting for Low-Level Structure Segmentations

https://github.com/nigelnnk/fairgad

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Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation

https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts

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A Practical Guide to Logical Access Voice Presentation Attack Detection

https://github.com/nikhilgalagali/adservice

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Practical data monitoring in the internet-services domain

https://github.com/nikitazozoulenko/kernel-timeseries-anomaly-detection

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Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection

https://github.com/nikolaiwest/pyscrew

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PyScrew: A Comprehensive Dataset Collection from Industrial Screw Driving Experiments

https://github.com/ninatu/anomaly_detection

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Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders

https://github.com/ninatu/piad

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Perceptual Image Anomaly Detection

https://github.com/niro-a/daeiocbpvgnn

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Detecting Anomalous Events in Object-centric Business Processes via Graph Neural Networks

https://github.com/niuchx/himnet

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Graph-level Anomaly Detection via Hierarchical Memory Networks

https://github.com/nivgold/TTAD

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Boosting Anomaly Detection Using Unsupervised Diverse Test-Time Augmentation

https://github.com/nla-group/fabba

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Quantized symbolic time series approximation

https://github.com/nmheim/torsk

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Adaptive Anomaly Detection in Chaotic Time Series with a Spatially Aware Echo State Network

https://github.com/noemiam/mist

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Tests for model misspecification in simulation-based inference: from local distortions to global model checks

https://github.com/nokia/LogGPT

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LogGPT: Log Anomaly Detection via GPT

https://github.com/nomewang/m3dm

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Multimodal Industrial Anomaly Detection via Hybrid Fusion

https://github.com/norlab-ulaval/perceptreev1

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Tree Detection and Diameter Estimation Based on Deep Learning

https://github.com/nothingtolose9979/ras

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Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly Detection

https://github.com/nsibf/pars-exad

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PARs: Predicate-based Association Rules for Efficient and Accurate Model-Agnostic Anomaly Explanation

https://github.com/nttcslab/ToyADMOS2-dataset

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ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions

https://github.com/nttcslab/dcase2023_task2_baseline_ae

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First-shot anomaly sound detection for machine condition monitoring: A domain generalization baseline

https://github.com/nubot-nudt/doss

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A Novel Decomposed Feature-Oriented Framework for Open-Set Semantic Segmentation on LiDAR Data

https://github.com/nuclearboy95/Anomaly-Detection-CKNN-PyTorch

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CKNN: Cleansed k-Nearest Neighbor for Unsupervised Video Anomaly Detection

https://github.com/nuclearboy95/Anomaly-Detection-PatchSVDD-PyTorch

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Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation

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