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/IsolationKernel/Codes

공식 코드

Subgraph Centralization: A Necessary Step for Graph Anomaly Detection

https://github.com/IvanOleksiyuk/transit-hep

공식 코드

TRANSIT your events into a new mass: Fast background interpolation for weakly-supervised anomaly searches

https://github.com/JJN123/Fall-Detection

공식 코드

DeepFall -- Non-invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders

https://github.com/JKU-ICG/AOS

공식 코드

Through-Foliage Tracking with Airborne Optical Sectioning

https://github.com/JeffersonLPLima/adversarial_oversampling

공식 코드

Heartbeat Anomaly Detection using Adversarial Oversampling

https://github.com/JetBrains-Research/kotlin-code-anomaly

공식 코드

Using Large-Scale Anomaly Detection on Code to Improve Kotlin Compiler

https://github.com/Jingtao-Li-CVer/TDD

공식 코드

One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning

https://github.com/Joaggi/anomaly-detection-density-matrix-kernel-density-estimation

공식 코드

AD-DMKDE: Anomaly Detection through Density Matrices and Fourier Features

https://github.com/Joaggi/lean-dmkde

공식 코드

LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection

https://github.com/Jonas1302/anomalib

공식 코드

Domain-independent detection of known anomalies

https://github.com/Jorge-Tejero-Fdez/ThreatLogLLM

공식 코드

Evaluating Language Models For Threat Detection in IoT Security Logs

https://github.com/Juintin/GAE-AT

공식 코드

On Generalization of Graph Autoencoders with Adversarial Training

https://github.com/Juintin/GraphAnomalyDetection

공식 코드

Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks

https://github.com/JuliaWolleb/DeScarGAN

공식 코드

DeScarGAN: Disease-Specific Anomaly Detection with Weak Supervision

https://github.com/JuneKyu/CLAD

공식 코드

What is Wrong with One-Class Anomaly Detection?

https://github.com/JustinTebbe/D3AD

공식 코드

Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection

https://github.com/JustinYuu/MACIL_SD

공식 코드

Modality-Aware Contrastive Instance Learning with Self-Distillation for Weakly-Supervised Audio-Visual Violence Detection

https://github.com/Key5n/IncFed-MDRS

공식 코드

Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection

https://github.com/KnowledgeDiscovery/OC4Seq

공식 코드

Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection

https://github.com/KnowledgeDiscovery/StrGNN

공식 코드

Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs

https://github.com/LJeub/Local2Global_embedding

공식 코드

Local2Global: A distributed approach for scaling representation learning on graphs

https://github.com/Leanna97/TSINR

공식 코드

TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection

https://github.com/LeapMind/MLF-SC

공식 코드

MLF-SC: Incorporating multi-layer features to sparse coding for anomaly detection

https://github.com/LeapMind/PUAD

공식 코드

PUAD: Frustratingly Simple Method for Robust Anomaly Detection

https://github.com/LeeDoYup/AnoGAN

공식 코드

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

https://github.com/Lemon-cmd/energy-transformer-graph

공식 코드

Energy Transformer

https://github.com/Lemon-cmd/energy-transformer-torch

공식 코드

Energy Transformer

https://github.com/LiDan456/GAN-AD

공식 코드

Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series

https://github.com/LiDan456/MAD-GANs

공식 코드

MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks

https://github.com/LiUzHiAn/hf2vad

공식 코드

A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction

https://github.com/LilyNLP/ADFAR

공식 코드

Defending Pre-trained Language Models from Adversarial Word Substitutions Without Performance Sacrifice

https://github.com/LinDuoming/LogEval

공식 코드

LogEval: A Comprehensive Benchmark Suite for Large Language Models In Log Analysis

https://github.com/LinaZhuang/RobustHyDenoiser-RhyDe-

공식 코드

Hyperspectral Image Denoising and Anomaly Detection Based on Low-rank and Sparse Representations

https://github.com/LucStrater/GeneralAD

공식 코드

GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features

https://github.com/Luca96/dark-autoencoders

공식 코드

Triggering Dark Showers with Conditional Dual Auto-Encoders

https://github.com/M-3LAB/Look-Inside-for-More

공식 코드

Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection

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