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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/M-Nassir/perception

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Anomaly Detection using Principles of Human Perception

https://github.com/MahsaMesgaran/GFCN

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Graph Fairing Convolutional Networks for Anomaly Detection

https://github.com/MasaKat0/D3RE

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Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation

https://github.com/MatanCohen1/Transformaly

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Transformaly -- Two (Feature Spaces) Are Better Than One

https://github.com/MateoLopez00/Imbalanced-Learning-Empirical-Evaluation

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Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced Datasets

https://github.com/Microsoft/EdgeML

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DROCC: Deep Robust One-Class Classification

https://github.com/MilanShao/ModeConv

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ModeConv: A Novel Convolution for Distinguishing Anomalous and Normal Structural Behavior

https://github.com/Mohammadakhavan75/KEAD

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Enhancing Anomaly Detection Generalization through Knowledge Exposure: The Dual Effects of Augmentation

https://github.com/MoonBlvd/Detection-of-Traffic-Anomaly

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When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos

https://github.com/MoonBlvd/tad-IROS2019

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Unsupervised Traffic Accident Detection in First-Person Videos

https://github.com/MyrtoLimnios/grad_2sample

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Concentration Inequalities for Two-Sample Rank Processes with Application to Bipartite Ranking

https://github.com/NSIBF/InvariantRuleAD

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Learning Invariant Rules from Data for Interpretable Anomaly Detection

https://github.com/NSIBF/NSIBF

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Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian Filtering

https://github.com/NarcissusEx/HETMM

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Hard-normal Example-aware Template Mutual Matching for Industrial Anomaly Detection

https://github.com/NazirNayal8/RbA

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RbA: Segmenting Unknown Regions Rejected by All

https://github.com/NeelKanwal/Vision-Transformers-for-Small-Histological-Datasets-Learned-Through-Knowledge-Distillation

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Vision Transformers for Small Histological Datasets Learned through Knowledge Distillation

https://github.com/Niousha12/Knowledge_Distillation_AD

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Multiresolution Knowledge Distillation for Anomaly Detection

https://github.com/Niousha12/Puzzle_Anomaly_Detection

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Puzzle-AE: Novelty Detection in Images through Solving Puzzles

https://github.com/Nirhoshan/NetDiffus

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NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series Imaging

https://github.com/NivC/SINBAD

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Set Features for Fine-grained Anomaly Detection

https://github.com/ORippler/gaussian-ad-mvtec

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

https://github.com/OliverHennhoefer/cross-conformal-anomaly-detection

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Uncertainty Quantification in Anomaly Detection with Cross-Conformal $p$-Values

https://github.com/OptimusPrimus/dcase2020_workshop

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Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier Examples

https://github.com/PanpanZheng/OCAN

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One-Class Adversarial Nets for Fraud Detection

https://github.com/ParitoshParmar/Fitness-AQA

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Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment

https://github.com/PaulaRuizB/FADE-ECG

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FADE: Forecasting for Anomaly Detection on ECG

https://github.com/PramuPerera/DeepOneClass

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Learning Deep Features for One-Class Classification

https://github.com/PrzemyslawStok/ssakg

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Associative Knowledge Graphs for Efficient Sequence Storage and Retrieval

https://github.com/Qi5Lei/STLM

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

https://github.com/Raymw/Federated-XGBoost

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The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost

https://github.com/ReginaMayhem/ClusterBusters

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Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant Network

https://github.com/Roc-Ng/XDVioDet

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Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision

https://github.com/RomeroBarata/skeleton_based_anomaly_detection

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Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos

https://github.com/RongrongMa/GLocalKD

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Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation

https://github.com/SAyaka0122/Deep-learning-based-binary-classifier

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Deep learning achieves perfect anomaly detection on 108,308 retinal images including unlearned diseases

https://github.com/SabanciParallelComputing/GOSH

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Understanding Coarsening for Embedding Large-Scale Graphs

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