https://github.com/arungansi/EfficientAD-1
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
Papers with Code 아카이브
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/arunppsg/TadGAN
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks
https://github.com/aryan-satpathy/backdoor
FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis
https://github.com/askarmel/SDAD
Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark
https://github.com/asm94/EfficientGAN
https://github.com/aubreychen9012/cAAE
Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders
https://github.com/aws-samples/amazon-sagemaker-edge-defect-detection-computer-vision
Mixed supervision for surface-defect detection: from weakly to fully supervised learning
https://github.com/awslabs/gluon-ts
https://github.com/awslabs/gluonts
https://github.com/baggepinnen/DynamicAxisWarping.jl
FastDTW is approximate and Generally Slower than the Algorithm it Approximates
https://github.com/bakirillov/capsules
Exploring Deep Anomaly Detection Methods Based on Capsule Net
https://github.com/benedekrozemberczki/karateclub
https://github.com/benradford/replication_arxiv_1803_10769
Network Traffic Anomaly Detection Using Recurrent Neural Networks
https://github.com/betterzhou/AAGNN
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
https://github.com/betterzhou/AugAN
Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation
https://github.com/bhatiasiddharth/MIDAS
https://github.com/billhhh/RDP
Unsupervised Representation Learning by Predicting Random Distances
https://github.com/black0017/black0017
Self-Supervised Anomaly Detection by Self-Distillation and Negative Sampling
https://github.com/blupblupblup/near-ood-doppler-signatures
https://github.com/bpucla/latent-space-EBM-prior
https://github.com/bsc-quantic/tn4ml/tree/master/docs/source/examples/tnad_latent
Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC
https://github.com/bsivanantham/VariationalAutoEncoder
https://github.com/bstienen/unsupervised-learning-metrics
How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?
https://github.com/buiduchanh/Learning-Deep-Features-for-One-Class-Classification
https://github.com/byungjae89/MahalanobisAD-pytorch
Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection
https://github.com/byungjae89/SPADE-pytorch
Sub-Image Anomaly Detection with Deep Pyramid Correspondences
https://github.com/canerturkmen/gluon-ts
https://github.com/caoyunkang/IKD
https://github.com/caoyunkang/WinClip
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
https://github.com/caoyunkang/groundedsam-zero-shot-anomaly-detection
Segment Any Anomaly without Training via Hybrid Prompt Regularization
https://github.com/captainfffsama/pathcore
https://github.com/caravagn/GDA
https://github.com/carbonati/variational-zoo
https://github.com/castacks/alfa-dataset
https://github.com/castacks/alfa-dataset-tools
https://github.com/cc-pine/telemanom
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.