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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/snetbl/potatoes

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Anomaly Detection With Partitioning Overfitting Autoencoder Ensembles

https://github.com/snipdome/nn_3d-anomaly-detection

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Voxel-wise classification for porosity investigation of additive manufactured parts with 3D unsupervised and (deeply) supervised neural networks

https://github.com/somepago/AMA

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Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders

https://github.com/sondsorb/tsad_eval

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Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Series

https://github.com/soumickmj/strega

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StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder

https://github.com/spirosmaggioros/ForestFires

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Wildfire danger prediction optimization with transfer learning

https://github.com/squareroot3/gadbench

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GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection

https://github.com/squareroot3/rethinking-anomaly-detection

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Rethinking Graph Neural Networks for Anomaly Detection

https://github.com/srikanthBezawada/OCCER

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One-Class Classification by Ensembles of Regression models -- a detailed study

https://github.com/ssarfraz/QuoVadisTAD

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Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?

https://github.com/sspring137/EagleEye

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Detecting Localized Density Anomalies in Multivariate Data via Coin-Flip Statistics

https://github.com/stanfordmimi/siaug

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Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays

https://github.com/steffenczolbe/topologicalchangedetection

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Spot the Difference: Detection of Topological Changes via Geometric Alignment

https://github.com/stephanieger/sequence-anomaly-detection

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Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification

https://github.com/steven-ccq/fast-temporal-motif

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Scalable Motif Counting for Large-scale Temporal Graphs

https://github.com/stevenliuwen/ano_pred_cvpr2018

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Future Frame Prediction for Anomaly Detection -- A New Baseline

https://github.com/stevenndungu/anomaly_detection

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Anomaly detection in radio galaxy data with trainable COSFIRE filters

https://github.com/sthoduka/motion_anomaly_detection

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Using Visual Anomaly Detection for Task Execution Monitoring

https://github.com/stitchkoala/stlm

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A SAM-guided Two-stream Lightweight Model for Anomaly Detection

https://github.com/suchun-sv/sgdd

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Does Graph Distillation See Like Vision Dataset Counterpart?

https://github.com/sukanyapatra1997/ForecastAD

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Detecting Abnormal Operations in Concentrated Solar Power Plants from Irregular Sequences of Thermal Images

https://github.com/sukanyapatra1997/ulsad-2024

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Revisiting Deep Feature Reconstruction for Logical and Structural Industrial Anomaly Detection

https://github.com/sullychen/smolk

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Sparse learned kernels for interpretable and efficient medical time series processing

https://github.com/summukhe/balanced_f1score

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Towards Unbiased Evaluation of Time-series Anomaly Detector

https://github.com/sungwool/cfa_for_anomaly_localization

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CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization

https://github.com/sunny5250/awesome-multi-setting-uiad

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A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Anomaly Detection

https://github.com/sunzc-sunny/ppad

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Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rays

https://github.com/sunzhihao18/ForgerySleuth

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ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation Detection

https://github.com/svrtk/masc-multi-task-segmentation-and-classification

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Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI

https://github.com/swyoon/diffusion-by-maxentirl

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Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models

https://github.com/swyoon/value-gradient-sampler

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Value Gradient Sampler: Sampling as Sequential Decision Making

https://github.com/sz85512678/signature_mahalanobis_knn

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Dimensionless Anomaly Detection on Multivariate Streams with Variance Norm and Path Signature

https://github.com/sztu-wearable/tiny-ppg

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Tiny-PPG: A Lightweight Deep Neural Network for Real-Time Detection of Motion Artifacts in Photoplethysmogram Signals on Edge Devices

https://github.com/t2mhanh/convlstm_prediction_anomalydetection

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Anomaly detection using prediction error with Spatio-Temporal Convolutional LSTM

https://github.com/tabguigui/cap

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Constrained Adaptive Projection with Pretrained Features for Anomaly Detection

https://github.com/tachyonicclock/surprisenet-cikm-23

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Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning

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