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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/jaideepmurkute/Multi-purpose-Disentangling-Variational-Autoencoders-for-ECG-data

Auto-Encoding Variational Bayes

https://github.com/jaime-varela/arxivFilterEmailer

Correlation-Driven Multi-Level Multimodal Learning for Anomaly Detection on Multiple Energy Sources

https://github.com/jainshobhit/Variational-Autoencoder

Auto-Encoding Variational Bayes

https://github.com/jarrydmartinx/generative-models

Auto-Encoding Variational Bayes

https://github.com/jayliu0313/Shape-Guided

https://github.com/jenyliu/DLA_interview

Auto-Encoding Variational Bayes

https://github.com/jerrywn121/TianChi_AIEarth

Is Space-Time Attention All You Need for Video Understanding?

https://github.com/jewelltaylor/OLED

OLED: One-Class Learned Encoder-Decoder Network with Adversarial Context Masking for Novelty Detection

https://github.com/jgasthaus/gluon-ts

GluonTS: Probabilistic Time Series Models in Python

https://github.com/jhchang/DFDC

Video Face Manipulation Detection Through Ensemble of CNNs

https://github.com/jhy12/inpainting-transformer

Inpainting Transformer for Anomaly Detection

https://github.com/jielin1997/Deep-Feature-Selection

Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection

https://github.com/jinghuazhao/Caprion

Auto-Encoding Variational Bayes

https://github.com/jmmartyn/neural-network-quantum-field-states

https://github.com/joe1chief/windownormalizaion

PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures

https://github.com/joetho786/Respiratory-Sound-Classification-in-Wearable-Devices-Enabled-by-Patient-Specific-Model-Tuning

Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning

https://github.com/johnpaparrizos/TSADSurvey

Dive into Time-Series Anomaly Detection: A Decade Review

https://github.com/jsiloto/dengAI

Deep and Confident Prediction for Time Series at Uber

https://github.com/judonini/DarkMachines

The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider

https://github.com/julian-wyatt/anoddpm

https://github.com/jun-cen/unified_open_set_recognition

Deep Anomaly Detection with Outlier Exposure

https://github.com/jun-jie-huang/lunar

Demystifying and Extracting Fault-indicating Information from Logs for Failure Diagnosis

https://github.com/jzwerling/SemanticKnowledgeGraph

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

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

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

https://github.com/kaize0409/meta-gdn

Few-shot Network Anomaly Detection via Cross-network Meta-learning

https://github.com/kampta/pytorch-distributions

Auto-Encoding Variational Bayes

https://github.com/kbountro/cSAX

Distribution Agnostic Symbolic Representations for Time Series Dimensionality Reduction and Online Anomaly Detection

https://github.com/keonlee9420/VAENAR-TTS

Glow: Generative Flow with Invertible 1x1 Convolutions

https://github.com/kevinwss/Deep-SAD-Baseline

Deep Semi-Supervised Anomaly Detection

https://github.com/khw11044/MY_GOAD

Deep Anomaly Detection Using Geometric Transformations

https://github.com/kidswithtokens/medsegdiff

MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model

https://github.com/kimmeen/sl-gad

Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection

https://github.com/kimmeen/time-llm

Transformers in Time Series: A Survey

https://github.com/kimx3314/PaDiM-Patch-Distribution-Modeling

PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization

https://github.com/kingcong/models/tree/main/stpm

Student-Teacher Feature Pyramid Matching for Anomaly Detection

https://github.com/kingjamessong/rankfeat

A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

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