Papers with Code 아카이브

Anomaly Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/mahindrautela/anovae-delamshm

공식 코드

Deep generative models for unsupervised delamination detection using guided waves

https://github.com/mahsunaltin/dr4mtsad

공식 코드

Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time Series

https://github.com/majitao-xd/bsdm-had

공식 코드

BSDM: Background Suppression Diffusion Model for Hyperspectral Anomaly Detection

https://github.com/majitao-xd/stad-had

공식 코드

Exploring Hyperspectral Anomaly Detection with Human Vision: A Small Target Aware Detector

https://github.com/maktukmak/mmfa

공식 코드

Multimodal Data Fusion in High-Dimensional Heterogeneous Datasets via Generative Models

https://github.com/mala-lab/UNPrompt

공식 코드

Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts

https://github.com/mala-lab/adshift

공식 코드

Anomaly Detection under Distribution Shift

https://github.com/mala-lab/ahl

공식 코드

Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection

https://github.com/mala-lab/anomalygfm

공식 코드

AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection

https://github.com/mala-lab/awesome-deep-graph-anomaly-detection

공식 코드

Deep Graph Anomaly Detection: A Survey and New Perspectives

https://github.com/mala-lab/faprompt

공식 코드

Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection

https://github.com/mala-lab/ggad

공식 코드

Generative Semi-supervised Graph Anomaly Detection

https://github.com/mala-lab/inctrl

공식 코드

Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts

https://github.com/mala-lab/prenet

공식 코드

Deep Weakly-supervised Anomaly Detection

https://github.com/mala-lab/sten

공식 코드

Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection

https://github.com/mala-lab/tam-master

공식 코드

Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection

https://github.com/mala-lab/winclip

공식 코드

Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts

https://github.com/malga-vision/modad

공식 코드

Looking at Model Debiasing through the Lens of Anomaly Detection

https://github.com/manitadayon/MTSCNeuralForecast

공식 코드

A clustering approach to time series forecasting using neural networks: A comparative study on distance-based vs. feature-based clustering methods

https://github.com/manuelszewc/null_hypothesis_test_for_anomaly_detection

공식 코드

Null Hypothesis Test for Anomaly Detection

https://github.com/marco-rudolph/ast

공식 코드

Asymmetric Student-Teacher Networks for Industrial Anomaly Detection

https://github.com/marco-rudolph/cs-flow

공식 코드

Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection

https://github.com/marco-rudolph/differnet

공식 코드

Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows

https://github.com/markscanlonucd/chatgpt-for-digital-forensics

공식 코드

ChatGPT for Digital Forensic Investigation: The Good, The Bad, and The Unknown

https://github.com/markushaug/acr-25

공식 코드

Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security Applications

https://github.com/mastavtsev/NLP-CLR-LogAnalyzer

공식 코드

NLP-Based .NET CLR Event Logs Analyzer

https://github.com/matanr/MLography

공식 코드

MLography: An Automated Quantitative Metallography Model for Impurities Anomaly Detection using Novel Data Mining and Deep Learning Approach

https://github.com/matejgrcic/DenseHybrid

공식 코드

DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition

https://github.com/matejgrcic/Open-set-M2F

공식 코드

On Advantages of Mask-level Recognition for Outlier-aware Segmentation

https://github.com/maticfuc/eccv_transfusion

공식 코드

TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection

https://github.com/matt-baugh/nnood

공식 코드

nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods

https://github.com/mattiacarletti/DIFFI

공식 코드

Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance

https://github.com/mattlaued/Optimal-Classification-Based-Unsupervised-Anomaly-Detection

공식 코드

Optimal Classification-based Anomaly Detection with Neural Networks: Theory and Practice

https://github.com/mattlaued/xor-is-linearly-classifiable

공식 코드

Revisiting Non-separable Binary Classification and its Applications in Anomaly Detection

https://github.com/mattswatson/attack-agnostic-adversarial-attack-detection

공식 코드

Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning

https://github.com/matutani/cooling-fan

공식 코드

Addressing Gap between Training Data and Deployed Environment by On-Device Learning

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