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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/ChunyuLiu188/SpectrumFM

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SpectrumFM: A Foundation Model for Intelligent Spectrum Management

https://github.com/ClancyZhou/P_Net_Anomaly_Detection

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Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images

https://github.com/D1aoBoomm/GI-PIP

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GI-PIP: Do We Require Impractical Auxiliary Dataset for Gradient Inversion Attacks?

https://github.com/DAMO-DI-ML/KDD2023-DCdetector

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DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection

https://github.com/DMLab-Tensor/SliceNStitch

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SliceNStitch: Continuous CP Decomposition of Sparse Tensor Streams

https://github.com/Darcyddx/Video-LLM

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Do Language Models Understand Time?

https://github.com/Dongzhijin/MNPWAD

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Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detection

https://github.com/DrMahdiRezaei/DeepSOCIAL

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DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

https://github.com/ENSTA-U2IS-AI/torch-uncertainty

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MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks

https://github.com/FardinJalilPiran/Differential-Privacy-Hyperdimensional-Computing

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Explainable Differential Privacy-Hyperdimensional Computing for Balancing Privacy and Transparency in Additive Manufacturing Monitoring

https://github.com/Faustinaqq/CKAAD

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Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial Learning

https://github.com/FedML-AI/FedIoT

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Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection

https://github.com/FinnBehrendt/Mahalanobis-Unsupervised-Anomaly-Detection

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Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly Detection

https://github.com/FlappyPeggy/DMAD

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Diversity-Measurable Anomaly Detection

https://github.com/Francois-Aubet/gluon-ts

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Neural Contextual Anomaly Detection for Time Series

https://github.com/Fraunhofer-AISEC/A3

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$\text{A}^3$: Activation Anomaly Analysis

https://github.com/Fraunhofer-AISEC/ARGUE

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Anomaly Detection by Recombining Gated Unsupervised Experts

https://github.com/Fraunhofer-AISEC/DA3D

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Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators

https://github.com/Fraunhofer-AISEC/R2-AD2

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R2-AD2: Detecting Anomalies by Analysing the Raw Gradient

https://github.com/GFNOrg/dt-gfn

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Learning Decision Trees as Amortized Structure Inference

https://github.com/GabMartino/icrst_trst_autoencoder

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Are We Using Autoencoders in a Wrong Way?

https://github.com/GitiHubi/deepContinualAuditing

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Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data

https://github.com/GiuMaz/AAMAS2021

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Identification of Unexpected Decisions in Partially Observable Monte-Carlo Planning: a Rule-Based Approach

https://github.com/Goldenhorns/FANFOLD

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FANFOLD: Graph Normalizing Flows-driven Asymmetric Network for Unsupervised Graph-Level Anomaly Detection

https://github.com/Gstaerman/ACHD

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The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure

https://github.com/Gstaerman/FIF

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Functional Isolation Forest

https://github.com/GuansongPang/deviation-network

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Deep Anomaly Detection with Deviation Networks

https://github.com/GuillaumeStaermanML/ACHD

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The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure

https://github.com/HHU-MMBS/plp-official-tmlr2024

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Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution Detection

https://github.com/HardikPrabhu/Energy-Time-series-anomaly-detection

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Generative Adversarial Network with Soft-Dynamic Time Warping and Parallel Reconstruction for Energy Time Series Anomaly Detection

https://github.com/HarveyCho/MCL

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Masked Contrastive Learning for Anomaly Detection

https://github.com/Hengwei-Zhao96/HOneCls

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One-Class Risk Estimation for One-Class Hyperspectral Image Classification

https://github.com/Hoangpham13579/end_to_end_anomaly_detection

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Benchmarking Jetson Edge Devices with an End-to-end Video-based Anomaly Detection System

https://github.com/HuiZhang0812/DiffusionAD-Denoising-Diffusion-for-Anomaly-Detection

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DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection

https://github.com/IdeasLabUT/EDA-Artifact-Detection

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Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data

https://github.com/IraKorshunova/bruno

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BRUNO: A Deep Recurrent Model for Exchangeable Data

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