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/alchemi5t/MNADrc

공식 코드

[Re] Learning Memory Guided Normality for Anomaly Detection

https://github.com/aleflabo/COSKAD

공식 코드

Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection

https://github.com/aleflabo/HypAD

공식 코드

Are we certain it's anomalous?

https://github.com/aleflabo/MoCoDAD

공식 코드

Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection

https://github.com/aleflabo/prego

공식 코드

PREGO: online mistake detection in PRocedural EGOcentric videos

https://github.com/alekssmolkovic/BuHuLaSpa

공식 코드

Bump Hunting in Latent Space

https://github.com/alessandro-f/dif-fuse

공식 코드

Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images

https://github.com/alessioarcudi/exiffi

공식 코드

Enhancing Interpretability and Generalizability in Extended Isolation Forests

https://github.com/alex-cse/NCF_AHSTD

공식 코드

Neural Collaborative Filtering to Detect Anomalies in Human Semantic Trajectories

https://github.com/alexanderfrotscher/andi

공식 코드

Unsupervised Anomaly Detection using Aggregated Normative Diffusion

https://github.com/alexandrosstergiou/TrajREC

공식 코드

Holistic Representation Learning for Multitask Trajectory Anomaly Detection

https://github.com/algue-rythme/oneclassmetriclearning

공식 코드

Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks

https://github.com/alibriee/paella

공식 코드

pAElla: Edge-AI based Real-Time Malware Detection in Data Centers

https://github.com/alinlab/CSI

공식 코드

CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances

https://github.com/alipay/microservice_system_twin_graph_based_anomaly_detection

공식 코드

Twin Graph-based Anomaly Detection via Attentive Multi-Modal Learning for Microservice System

https://github.com/alirezasalehy/crane

공식 코드

Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detections

https://github.com/allanchen95/gccad

공식 코드

Graph Contrastive Learning for Anomaly Detection

https://github.com/allenyljiang/anomaly-detection-in-sequences

공식 코드

Reasonable Anomaly Detection in Long Sequences

https://github.com/alshami52/COOOL_benchmark

공식 코드

COOOL: Challenge Of Out-Of-Label A Novel Benchmark for Autonomous Driving

https://github.com/amazon-research/hyperbolic-embeddings

공식 코드

Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs

https://github.com/amazon-research/long-tailed-ood-detection

공식 코드

Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition

https://github.com/amazon-research/patchcore-inspection

공식 코드

Towards Total Recall in Industrial Anomaly Detection

https://github.com/amigolab/synthanatomy

공식 코드

Morphology-preserving Autoregressive 3D Generative Modelling of the Brain

https://github.com/amirmk89/gepc

공식 코드

Graph Embedded Pose Clustering for Anomaly Detection

https://github.com/ammarlodhi255/pediatric_wrist_abnormality_detection-end-to-end-implementation

공식 코드

Enhancing Wrist Fracture Detection with YOLO

https://github.com/amsehili/mvtsevalpaper

공식 코드

Multivariate Time Series Anomaly Detection: Fancy Algorithms and Flawed Evaluation Methodology

https://github.com/anasemad11/c2fpl

공식 코드

A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection

https://github.com/anasemad11/clap

공식 코드

Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline

https://github.com/andreabiondapolimi/anomaly-detection-autoencoders-using-cw-ssim

공식 코드

Deep Autoencoders for Anomaly Detection in Textured Images using CW-SSIM

https://github.com/andreartelt/sam-knn-regressor_onlinelearning_wdns

공식 코드

SAM-kNN Regressor for Online Learning in Water Distribution Networks

https://github.com/andreluizbvs/insplad

공식 코드

InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images

https://github.com/andrespulido8/sonar_sparse_point_cloud

공식 코드

Time and Cost-Efficient Bathymetric Mapping System using Sparse Point Cloud Generation and Automatic Object Detection

https://github.com/andreyoo/ncae_uad

공식 코드

Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination

https://github.com/andyzoujm/pixmix

공식 코드

PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures

https://github.com/anhduy0911/coinception

공식 코드

Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient Encoder

https://github.com/anilosmantur/conditioned_video_anomaly_diffusion

공식 코드

Unsupervised Video Anomaly Detection with Diffusion Models Conditioned on Compact Motion Representations

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