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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/dnguyengithub/MultitaskAIS

공식 코드

A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams

https://github.com/donaldrr/simplenet

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SimpleNet: A Simple Network for Image Anomaly Detection and Localization

https://github.com/dong03/MVSS-Net

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Image Manipulation Detection by Multi-View Multi-Scale Supervision

https://github.com/donghao51/nng-mix

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NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation

https://github.com/donghyung87/FAPM_official

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FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly Detection

https://github.com/dongtsi/deepaid

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DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications

https://github.com/dorisbao/bmad

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BMAD: Benchmarks for Medical Anomaly Detection

https://github.com/dreizehnutters/pcapae

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Representation Learning for Content-Sensitive Anomaly Detection in Industrial Networks

https://github.com/dsba-lab/rapid

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RAPID: Training-free Retrieval-based Log Anomaly Detection with PLM considering Token-level information

https://github.com/dspoka/mnm

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An Empirical Investigation of Contextualized Number Prediction

https://github.com/dual-grp/fedpca_anomalydetection

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Federated PCA on Grassmann Manifold for IoT Anomaly Detection

https://github.com/duiliod/deepnorha

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Learning normal asymmetry representations for homologous brain structures

https://github.com/dzambon/cdg

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Concept Drift and Anomaly Detection in Graph Streams

https://github.com/easyoo/omni-ad

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Omni-AD: Learning to Reconstruct Global and Local Features for Multi-class Anomaly Detection

https://github.com/eavnjeong/ief-vad

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Uncertainty-Weighted Image-Event Multimodal Fusion for Video Anomaly Detection

https://github.com/ebagdasa/backdoor_federated_learning

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How To Backdoor Federated Learning

https://github.com/eccv22-ood-workshop/eccv22-ood-workshop.github.io

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Advancing Image Retrieval with Few-Shot Learning and Relevance Feedback

https://github.com/edadaltocg/ood-trajectory-projection

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A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution Detection

https://github.com/eddardd/MROT

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Unsupervised Anomaly Detection through Mass Repulsing Optimal Transport

https://github.com/ehtesam3154/dgs_svdd

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Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical Systems

https://github.com/ejokhan/transnas_tsad

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TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly Detection

https://github.com/eleutherai/elk-generalization

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Eliciting Latent Knowledge from Quirky Language Models

https://github.com/eliahuhorwitz/3D-ADS

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Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection

https://github.com/elischwartz/maeday

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MAEDAY: MAE for few and zero shot AnomalY-Detection

https://github.com/elisejiuqizhang/RLMSAD

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Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection

https://github.com/elki-project/elki

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ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"

https://github.com/emadeldeen24/tslanet

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TSLANet: Rethinking Transformers for Time Series Representation Learning

https://github.com/eml-eda/tle-supervised

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Foundation Models for Structural Health Monitoring

https://github.com/emorzz1g/patchad

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PatchAD: A Lightweight Patch-based MLP-Mixer for Time Series Anomaly Detection

https://github.com/emorzz1g/simad

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SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly Detection

https://github.com/emundo/ecgan

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Adversarial Anomaly Detection using Gaussian Priors and Nonlinear Anomaly Scores

https://github.com/endtheme123/vae-compare

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Variational Autoencoder for Anomaly Detection: A Comparative Study

https://github.com/enquanyang2022/3cad

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3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised Anomaly

https://github.com/enyandai/ganf

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Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series

https://github.com/eqtpartners/tsde

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Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask

https://github.com/ericjang/odin

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WAIC, but Why? Generative Ensembles for Robust Anomaly Detection

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