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Anomaly Detection
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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/lmb-freiburg/diffusion-for-ood
공식 코드Diffusion for Out-of-Distribution Detection on Road Scenes and Beyond
https://github.com/logintelligence/logadempirical
공식 코드Log-based Anomaly Detection with Deep Learning: How Far Are We?
https://github.com/logpai/deep-loglizer
공식 코드Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection
https://github.com/lohrera/comadout
공식 코드CoMadOut -- A Robust Outlier Detection Algorithm based on CoMAD
https://github.com/lohrera/gadformer
공식 코드GADformer: A Transparent Transformer Model for Group Anomaly Detection on Trajectories
https://github.com/longkaifang/3d-adnas
공식 코드Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural Perspective
https://github.com/lorenapoenaru/anomaly_detection
공식 코드Is Your Anomaly Detector Ready for Change? Adapting AIOps Solutions to the Real World
https://github.com/lorenzo-perini/ballad
공식 코드https://github.com/lorenzo-perini/gammagmm
공식 코드Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection
https://github.com/lsky96/unrolled-tbsca-ad
공식 코드Adaptive Anomaly Detection in Network Flows with Low-Rank Tensor Decompositions and Deep Unrolling
https://github.com/luca-zanella-dvl/AnomalyCLIP
공식 코드Delving into CLIP latent space for Video Anomaly Recognition
https://github.com/lucasponteslpa/QOCClassifier
공식 코드Quantum One-class Classification With a Distance-based Classifier
https://github.com/luigy-mach/trade
공식 코드TrADe Re-ID -- Live Person Re-Identification using Tracking and Anomaly Detection
https://github.com/luissouto/aida
공식 코드AIDA: Analytic Isolation and Distance-based Anomaly Detection Algorithm
https://github.com/lukasruff/Classification-AD
공식 코드https://github.com/lukasruff/Deep-SAD-PyTorch
공식 코드https://github.com/lunyiliu/LogLM
공식 코드LogLM: From Task-based to Instruction-based Automated Log Analysis
https://github.com/lunyiliu/logprompt
공식 코드Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies
https://github.com/luow23/ami-net
공식 코드AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization
https://github.com/luow23/inp-former
공식 코드Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection
https://github.com/lyh-hh/afire
공식 코드https://github.com/lyn1874/daml
공식 코드Decoupled Appearance and Motion Learning for Efficient Anomaly Detection in Surveillance Video
https://github.com/lyx199504/f-se-lstm-time-series
공식 코드F-SE-LSTM: A Time Series Anomaly Detection Method with Frequency Domain Information
https://github.com/m-3lab/awesome-industrial-anomaly-detection
공식 코드https://github.com/m-3lab/awesome-visual-sensory-anomaly-detection
공식 코드https://github.com/m-3lab/im-iad
공식 코드IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
https://github.com/m-3lab/open-iad
공식 코드IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
https://github.com/m-3lab/real3d-ad
공식 코드https://github.com/m-code-space/sadde
공식 코드SADDE: Semi-supervised Anomaly Detection with Dependable Explanations
https://github.com/m-kruse98/multi-flow
공식 코드Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly Detection
https://github.com/m-kruse98/splatpose
공식 코드https://github.com/m-nassir/clustering
공식 코드A Computational Theory and Semi-Supervised Algorithm for Clustering
https://github.com/madharnisrine/pcann
공식 코드Anomaly Detection on Financial Time Series by Principal Component Analysis and Neural Networks
https://github.com/mahfuzmohammad/brainomaly
공식 코드https://github.com/mahfuzmohammad/healthygan
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.