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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/benedictstar/Joint-VAD

공식 코드

Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detection

https://github.com/beyond-zw/dlan-ac

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Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection

https://github.com/bghojogh/Curvature-Anomaly-Detection

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Anomaly Detection and Prototype Selection Using Polyhedron Curvature

https://github.com/bghojogh/iMondrian

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Isolation Mondrian Forest for Batch and Online Anomaly Detection

https://github.com/bharathsudharsan/cnn_on_mcu

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Multi-Component Optimization and Efficient Deployment of Neural-Networks on Resource-Constrained IoT Hardware

https://github.com/bhooi/dynamic

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Dynamic Graph-Based Anomaly Detection in the Electrical Grid

https://github.com/bhoov/energy-transformer-jax

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Energy Transformer

https://github.com/billpwchan/DeepTrust

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DeepTrust: A Reliable Financial Knowledge Retrieval Framework For Explaining Extreme Pricing Anomalies

https://github.com/bingleilou/fsead

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fSEAD: a Composable FPGA-based Streaming Ensemble Anomaly Detection Library

https://github.com/bit-ml/anoshift

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AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection

https://github.com/bit-ml/date

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DATE: Detecting Anomalies in Text via Self-Supervision of Transformers

https://github.com/blacksingular/wsdm_gdn

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Alleviating Structural Distribution Shift in Graph Anomaly Detection

https://github.com/blaz-r/supersimplenet

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SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection

https://github.com/blingcho/vflip-esorics24

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VFLIP: A Backdoor Defense for Vertical Federated Learning via Identification and Purification

https://github.com/bluorion-com/ZClip

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ZClip: Adaptive Spike Mitigation for LLM Pre-Training

https://github.com/bmvc-fade/bmvc-fade

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FADE: Few-shot/zero-shot Anomaly Detection Engine using Large Vision-Language Model

https://github.com/bnachman/DCTRHunting

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Simulation Assisted Likelihood-free Anomaly Detection

https://github.com/bnagy/hexml-paper

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Metre as a stylometric feature in Latin hexameter poetry

https://github.com/boehringer-ingelheim/anomaly-detection-in-histology

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Learning image representations for anomaly detection: application to discovery of histological alterations in drug development

https://github.com/bombooooo/cnts

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CNTS: Cooperative Network for Time Series

https://github.com/boortel/ae-reconstruction-and-feature-based-ad

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Feature space reduction as data preprocessing for the anomaly detection

https://github.com/boortel/sift-and-surf-based-ad

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SIFT and SURF based feature extraction for the anomaly detection

https://github.com/boschresearch/LatentOE-AD

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Latent Outlier Exposure for Anomaly Detection with Contaminated Data

https://github.com/boschresearch/NeuTraL-AD

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Neural Transformation Learning for Deep Anomaly Detection Beyond Images

https://github.com/boschresearch/graphlevel-anomalydetection

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Raising the Bar in Graph-level Anomaly Detection

https://github.com/boschresearch/hierarchical_anomaly_detection

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Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features

https://github.com/boschresearch/local_neural_transformations

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Detecting Anomalies within Time Series using Local Neural Transformations

https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge

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The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider

https://github.com/briviosimone/sae_eys

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Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective

https://github.com/bychelsea/vand-april-gan

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APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD

https://github.com/ca1man-2022/awesome-good-detection

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Out-of-Distribution Detection on Graphs: A Survey

https://github.com/caiyu6666/ae4ad

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Rethinking Autoencoders for Medical Anomaly Detection from A Theoretical Perspective

https://github.com/caiyu6666/ddad

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Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays

https://github.com/caiyu6666/ddad-asr

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Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images

https://github.com/caiyu6666/medianomaly

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MedIAnomaly: A comparative study of anomaly detection in medical images

https://github.com/caiyuxuan1120/daf

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A Discrepancy Aware Framework for Robust Anomaly Detection

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