Anomaly Detection
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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/adverml/advopenood
https://github.com/afraea/q-anomaly
https://github.com/agnesdeng/misle
https://github.com/ai4risk/antifraud
https://github.com/aisinai/seeg_analysis
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
https://github.com/akshu281/KDD_LSTM
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
https://github.com/alacrity2001/PTSA-public-
https://github.com/albertkx/conal
https://github.com/albertwujj/MSCRED
https://github.com/alexbooth/Beta-VAE-Tensorflow-2.0
https://github.com/alexey-pronkin/annealed
https://github.com/alexisbdr/warehouse-anomaly
https://github.com/alexriedel1/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/alinazhiltsova/Replication_arxiv_1803_10769
Network Traffic Anomaly Detection Using Recurrent Neural Networks
https://github.com/allnightlight/ConditionalWassersteinAutoencoderPoweredBySinkhornDistance
https://github.com/amandaberg/GANanomalyDetection
https://github.com/amazon-science/patchcore-inspection
https://github.com/amazon-science/spot-diff
SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation
https://github.com/andongdeng/bear
https://github.com/angzhifan/Auto-Encoding_Variational_Bayes
https://github.com/aninair1905/Abnormal-Event-Detection
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
https://github.com/anirudh2019/MURA-xception
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
https://github.com/anirudh2019/MURA-xception-inceptionV2
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
https://github.com/antoinedemathelin/openood
https://github.com/any-tech/PatchCore-ex
https://github.com/any-tech/SPADE-fast
Sub-Image Anomaly Detection with Deep Pyramid Correspondences
https://github.com/aoru45/Deep-Anomaly-Detection-for-Generalized-Face-Anti-Spoofing
https://github.com/aotumanbiu/OC-NN
https://github.com/arahosu/musicslots
https://github.com/areylng/MemSeg
https://github.com/areylng/SPADE-MVTEC-LOCO-AD-dataset
Sub-Image Anomaly Detection with Deep Pyramid Correspondences
https://github.com/ariaghora/robust-period
RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity Detection
https://github.com/arminarj/FashionMNIST_VAE
https://github.com/arogers1/VAE_LSTM_Text_Encoding
https://github.com/arungansi/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.