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Out Of Distribution Detection On Imagenet 1K 12

Métriques

AUROC

FPR95

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	AUROC	FPR95	Paper Title	Repository
MOS (BiT-S-R101x1)	90.11	39.97	MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space
ASH-S (ResNet-50)	95.12	22.8	Extremely Simple Activation Shaping for Out-of-Distribution Detection
NPOS	91.22	37.93	Non-Parametric Outlier Synthesis
LINe (ResNet-50)	95.03	20.70	LINe: Out-of-Distribution Detection by Leveraging Important Neurons
NNGuide (RegNet)	95.42	17.97	Nearest Neighbor Guidance for Out-of-Distribution Detection
RP+GradNorm	-	70.12	Detecting Out-of-distribution Data through In-distribution Class Prior
DOE	83.54	59.83	Out-of-distribution Detection with Implicit Outlier Transformation
DML	-	54.74	Decoupling MaxLogit for Out-of-Distribution Detection	-
SCALE (ResNet50)	95.71	20.05	Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement	-
RankFeat (ResNetv2-101)	92.15	36.8	RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection
MOOD	89.1	-	Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need
ODIN+UMAP (ResNet-50)	89.24	40.94	Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability
NNGuide (ResNet50 w/ ReAct)	95.45	19.72	Nearest Neighbor Guidance for Out-of-Distribution Detection
DICE + ReAct (ResNet-50)	93.4	27.25	DICE: Leveraging Sparsification for Out-of-Distribution Detection
BATS (ResNet-50)	94.28	27.11	Boosting Out-of-distribution Detection with Typical Features	-
MCM (CLIP-L)	91.49	38.17	Delving into Out-of-Distribution Detection with Vision-Language Representations

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