Out-of-Distribution Detection on CIFAR-10

Metric: AUROC (higher is better)

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#	Model↕	AUROC▼	Extra Data	Paper	Date↕	Code
1	DHM	100	No	-	-	-
2	Wide ResNet 40x2	99.9	Yes	An Effective Baseline for Robustness to Distribu...	2021-05-15	Code
3	ResNet 34 + OECC+GM	99.7	Yes	Outlier Exposure with Confidence Control for Out...	2019-06-08	Code
4	ResNet 34 + FSSD	99.5	No	Feature Space Singularity for Out-of-Distributio...	2020-11-30	Code
5	Wide ResNet 40x2	99.43	No	RODD: A Self-Supervised Approach for Robust Out-...	2022-04-06	Code
6	ZODE-KNN	99.12	No	Boosting Out-of-Distribution Detection with Mult...	2022-12-24	Code
7	ResNet18 + APR-P	98.1	No	Amplitude-Phase Recombination: Rethinking Robust...	2021-08-19	Code
8	WRN 40-2 + OE	97.8	Yes	Deep Anomaly Detection with Outlier Exposure	2018-12-11	Code
9	WRN 40-2 (MSP Baseline)	97.8	No	Deep Anomaly Detection with Outlier Exposure	2018-12-11	Code
10	WRN 40-2 + Rotation Prediction	96.2	No	Using Self-Supervised Learning Can Improve Model...	2019-06-28	Code

#1DHM
100
AUROC
No paper
#2Wide ResNet 40x2SOTA
99.9
AUROC· Extra Data· 2021-05-15
An Effective Baseline for Robustness to Distributional Shift Code
#3ResNet 34 + OECC+GMSOTA
99.7
AUROC· Extra Data· 2019-06-08
Outlier Exposure with Confidence Control for Out-of-Distribution Detection Code
#4ResNet 34 + FSSD
99.5
AUROC· 2020-11-30
Feature Space Singularity for Out-of-Distribution Detection Code
#5Wide ResNet 40x2
99.43
AUROC· 2022-04-06
RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection Code
#6ZODE-KNN
99.12
AUROC· 2022-12-24
Boosting Out-of-Distribution Detection with Multiple Pre-trained Models Code
#7ResNet18 + APR-P
98.1
AUROC· 2021-08-19
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain Code
#8WRN 40-2 + OESOTA
97.8
AUROC· Extra Data· 2018-12-11
Deep Anomaly Detection with Outlier Exposure Code
#9WRN 40-2 (MSP Baseline)
97.8
AUROC· 2018-12-11
Deep Anomaly Detection with Outlier Exposure Code
#10WRN 40-2 + Rotation Prediction
96.2
AUROC· 2019-06-28
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty Code