Anomaly Detection On One Class Cifar 10

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AUROC

Résultats

Résultats de performance de divers modèles sur ce benchmark

		Paper Title	Repository
CLIP (OE)	99.6	Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
GeneralAD	99.3	GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features
Fake It Till You Make It	99.1	Fake It Till You Make It: Towards Accurate Near-Distribution Novelty Detection
BLISS	99.1	When Text and Images Don't Mix: Bias-Correcting Language-Image Similarity Scores for Anomaly Detection	-
PANDA-OE	98.9	PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
Mean-Shifted Contrastive Loss	98.6	Mean-Shifted Contrastive Loss for Anomaly Detection
CLIP (zero shot)	98.5	Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
DINO-FT	98.4	Anomaly Detection Requires Better Representations
Transformaly	98.3	Transformaly -- Two (Feature Spaces) Are Better Than One
CAP	97.0	Constrained Adaptive Projection with Pretrained Features for Anomaly Detection
PANDA	96.2	PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
CSI	94.3	CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
DUIAD	92.6	Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework	-
DN2	92.5	Deep Nearest Neighbor Anomaly Detection	-
DisAug CLR	92.5	Learning and Evaluating Representations for Deep One-class Classification
FCDD	92	Explainable Deep One-Class Classification
IGD (pre-trained SSL)	91.25	Deep One-Class Classification via Interpolated Gaussian Descriptor
GAN based Anomaly Detection in Imbalance Problems	90.6	GAN-based Anomaly Detection in Imbalance Problems	-
SSOOD	90.1	Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
SSD	90.0	SSD: A Unified Framework for Self-Supervised Outlier Detection

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Anomaly Detection On One Class Cifar 10

Métriques

Résultats