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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/tae-mo/GRAD

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Continuous Memory Representation for Anomaly Detection

https://github.com/taherehzarratehsan/chicken-behavior-analysis

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Broiler-Net: A Deep Convolutional Framework for Broiler Behavior Analysis in Poultry Houses

https://github.com/takahashihiroshi/deep-puad

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Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data

https://github.com/takashiishida/pconf

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Binary Classification from Positive-Confidence Data

https://github.com/talreiss/Mean-Shifted-Anomaly-Detection

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Mean-Shifted Contrastive Loss for Anomaly Detection

https://github.com/talreiss/PANDA

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PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation

https://github.com/talreiss/accurate-interpretable-vad

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An Attribute-based Method for Video Anomaly Detection

https://github.com/talreiss/coldfusion

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From Zero to Hero: Cold-Start Anomaly Detection

https://github.com/tardelean/blindlca

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Blind Localization and Clustering of Anomalies in Textures

https://github.com/taslimisina/osr-ood-ad-methods

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A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges

https://github.com/tbw162/aa-bigan

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Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANs

https://github.com/tbw162/purigan

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Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks

https://github.com/tcs211/ai_eeeg_report

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A Hybrid Artificial Intelligence System for Automated EEG Background Analysis and Report Generation

https://github.com/tecsar-uncc/chad

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CHAD: Charlotte Anomaly Dataset

https://github.com/tecsar-uncc/pheva

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PHEVA: A Privacy-preserving Human-centric Video Anomaly Detection Dataset

https://github.com/tecsar-uncc/poselift

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Exploring Pose-Based Anomaly Detection for Retail Security: A Real-World Shoplifting Dataset and Benchmark

https://github.com/tecsar-uncc/tsgad

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An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Prediction

https://github.com/tfahim15/had2024

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Hyperedge Anomaly Detection with Hypergraph Neural Network

https://github.com/thedatumorg/vus

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VUS: Effective and Efficient Accuracy Measures for Time-Series Anomaly Detection

https://github.com/thomasfoltz/VADSK

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Video Anomaly Detection with Structured Keywords

https://github.com/thu-spmi/Inclusive-NRF

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Generative Modeling by Inclusive Neural Random Fields with Applications in Image Generation and Anomaly Detection

https://github.com/thudm/graphcad

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Graph Contrastive Learning for Anomaly Detection

https://github.com/thufdd/jiaolongdsms_datasets

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Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and Techniques

https://github.com/thufdd/thu-concept-drift-datasets

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Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and Techniques

https://github.com/thuml/Anomaly-Transformer

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Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

https://github.com/thuml/Large-Time-Series-Model

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Timer: Generative Pre-trained Transformers Are Large Time Series Models

https://github.com/tian0426/frera

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FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series Classification

https://github.com/tianchenji/Multimodal-SVAE

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Multi-Modal Anomaly Detection for Unstructured and Uncertain Environments

https://github.com/tianchenji/paad

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Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusion

https://github.com/tiangexiang/squid

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SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection

https://github.com/tianyu0207/CCD

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Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images

https://github.com/tianyu0207/FSAD-Net

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Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy

https://github.com/tianyu0207/IGD

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Deep One-Class Classification via Interpolated Gaussian Descriptor

https://github.com/tianyu0207/MemMC-MAE

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Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder

https://github.com/tianyu0207/RTFM

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Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning

https://github.com/tianyu0207/pebal

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Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes

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