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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/cakcora/PeaceCorps

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Smart Vectorizations for Single and Multiparameter Persistence

https://github.com/cambridgecia/cytodiffusion

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Deep Generative Classification of Blood Cell Morphology

https://github.com/campanulabells/prem-gad

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PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection

https://github.com/caoyunkang/CDO

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Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization

https://github.com/caoyunkang/CPMF

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Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection

https://github.com/caoyunkang/VarAD

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VarAD: Lightweight High-Resolution Image Anomaly Detection via Visual Autoregressive Modeling

https://github.com/caoyunkang/adaclip

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AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection

https://github.com/caoyunkang/gpt4v-for-generic-anomaly-detection

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Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead

https://github.com/caoyunkang/segment-any-anomaly

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Segment Any Anomaly without Training via Hybrid Prompt Regularization

https://github.com/capwidow/w-transformer

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W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting

https://github.com/careerbuilder/semantic-knowledge-graph

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The Semantic Knowledge Graph: A compact, auto-generated model for real-time traversal and ranking of any relationship within a domain

https://github.com/carlosjva/pad

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A Subspace Method for Time Series Anomaly Detection in Cyber-Physical Systems

https://github.com/carolchenyx/mgfn

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MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection

https://github.com/carrtesy/m2n2

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When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detection

https://github.com/casia-iva-lab/anomalygpt

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AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models

https://github.com/casia-iva-lab/filo

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FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization

https://github.com/catchxu/acsleuth

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Domain Adaptive and Fine-grained Anomaly Detection for Single-cell Sequencing Data and Beyond

https://github.com/cbmi-group/adfa

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ADFA: Attention-augmented Differentiable top-k Feature Adaptation for Unsupervised Medical Anomaly Detection

https://github.com/cchallu/dghl

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Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection

https://github.com/cfeng16/audio-visual-forensics

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Self-Supervised Video Forensics by Audio-Visual Anomaly Detection

https://github.com/chadhgy/cam

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Learning Cortical Anomaly through Masked Encoding for Unsupervised Heterogeneity Mapping

https://github.com/changgang-zheng/planter

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Automating In-Network Machine Learning

https://github.com/changwoo-lee/TLOHO

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T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on Graphs

https://github.com/changzong/STCAD

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Structural-Temporal Coupling Anomaly Detection with Dynamic Graph Transformer

https://github.com/charlesge/openood-cadet

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CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning

https://github.com/chenguolin/nutime

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NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretraining

https://github.com/chenwang4/oilad

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OIL-AD: An Anomaly Detection Framework for Sequential Decision Sequences

https://github.com/chenyirui/gim

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GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization

https://github.com/chenyuanTKCY/KDSelector

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KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly Detection

https://github.com/chiarabales/unsupervised_sv

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Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data

https://github.com/chihchenghsieh/multimodal-abnormalities-detection

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MDF-Net for abnormality detection by fusing X-rays with clinical data

https://github.com/chopper-233/anomaly-shapenet

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Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network

https://github.com/choubo/dra

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Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection

https://github.com/chufangao/glue

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Learning Graph Neural Networks for Multivariate Time Series Anomaly Detection

https://github.com/ci-ber/ra

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Towards Universal Unsupervised Anomaly Detection in Medical Imaging

https://github.com/ci-ber/thor_ddpm

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Diffusion Models with Implicit Guidance for Medical Anomaly Detection

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