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异常检测
异常检测是一种二分类任务,旨在识别数据集中显著偏离大多数数据的不寻常或意外模式。该任务的目标是发现这些异常点,它们可能代表错误、欺诈或其他类型的异常事件,并对其进行标记以便进一步调查。异常检测在金融风控、网络安全、医疗诊断等领域具有重要应用价值。
排行榜
共计 100 个模型
基准测试
MVTec AD
GLASS
VisA
ReContrast
MVTec LOCO AD
CSAD
One-class CIFAR-10
CSI
CUHK Avenue
HF2VAD+SSPCAB
ShanghaiTech
SSMTL+UBnormal
UCR Anomaly Archive
Auto-Encoder with Regression (AER)
Fishyscapes L&F
cDNP+OE
MPDD
GLASS
One-class CIFAR-100
GeneralAD
BTAD
MuSc (zero-shot)
UBnormal
TimeSformer
Unlabeled CIFAR-10 vs CIFAR-100
CSI
UCSD Ped2
Background-Agnostic
Fashion-MNIST
One-class ImageNet-30
CSI
Road Anomaly
RbA
Numenta Anomaly Benchmark
HTM AL
AeBAD-S
MSFR
Fishyscapes
RPL+CoroCL
Anomaly Detection on Unlabeled CIFAR-10 vs LSUN (Fix)
AeBAD-V
MMR
Hyper-Kvasir Dataset
Leave-One-Class-Out CIFAR-10
Leave-One-Class-Out ImageNet-30
BCE-CLIP (OE)
MNIST
InsPLAD
AttentDifferNet (SENet-AlexNet)
Anomaly Detection on Anomaly Detection on Unlabeled ImageNet-30 vs Flowers-102
Anomaly Detection on Unlabeled ImageNet-30 vs CUB-200
LAG
CCD
DIOR
Self-Supervised One-class SVM, RBF kernel
MVTEC AD textures
PHEVA
MPED-RNN
Lost and Found
Cats-and-Dogs
Self-Supervised One-class SVM, RBF kernel
Surface Defect Saliency of Magnetic Tile
HETMM
UEA time-series datasets
SINBAD
ODDS
kNN
voraus-AD
MVT-Flow
UCSD Peds2
Corridor
Two-stream
MVTec AD Textures Domain Generalization
FABLE
PAD Dataset
SplatPose
Vehicle Claims
Random Forest
Thyroid
RCALAD
MVTEC 3D-AD
CDO
MVTec-AD
MVTec LOCO
COCO-OOC
kdd 99
PCA via oversampling