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Out Of Distribution Detection On Imagenet 1K 10
Out Of Distribution Detection On Imagenet 1K 10
評価指標
AUROC
FPR95
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
AUROC
FPR95
Paper Title
Repository
DICE + ReAct (ResNet-50)
92.74
28.07
DICE: Leveraging Sparsification for Out-of-Distribution Detection
-
GradNorm (ResNetv2-101)
-
61.42
On the Importance of Gradients for Detecting Distributional Shifts in the Wild
-
LINe (ResNet-50)
94.44
22.54
LINe: Out-of-Distribution Detection by Leveraging Important Neurons
-
Watermarking (WRN-40-2 w/ MSP)
84.00
61.2
Watermarking for Out-of-distribution Detection
-
Watermarking (WRN-40-2 w/ Energy)
80.80
67.75
Watermarking for Out-of-distribution Detection
-
MOS (BiT-S-R101x1)
81.23
60.43
MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space
-
ViM (ViT-B/16)
95.34
20.31
ViM: Out-Of-Distribution with Virtual-logit Matching
-
ODIN+UMAP (ResNet-50)
88.35
42.02
Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability
-
DOE
88.9
34.67
Out-of-distribution Detection with Implicit Outlier Transformation
-
GEN
-
77.93
GEN: Pushing the Limits of Softmax-Based Out-of-Distribution Detection
ViM (BiT-S-R101×1)
-
4.69
ViM: Out-Of-Distribution with Virtual-logit Matching
-
NAC-UE (ResNet-50)
97.9
-
Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization
-
BATS (ResNet-50)
92.27
38.9
Boosting Out-of-distribution Detection with Typical Features
-
SSD
85.4
57.2
SSD: A Unified Framework for Self-Supervised Outlier Detection
-
KNN (ResNet-50 SupCon)
94.45
16.97
Out-of-Distribution Detection with Deep Nearest Neighbors
-
NNGuide (RegNet)
95.82
17.00
Nearest Neighbor Guidance for Out-of-Distribution Detection
-
MOOD
91.3
-
Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need
-
RP+GradNorm
-
79.80
Detecting Out-of-distribution Data through In-distribution Class Prior
KNN (ResNet-50)
97.18
11.56
Out-of-Distribution Detection with Deep Nearest Neighbors
-
SHE
-
45.09
Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield Energy
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