Single-Source Domain Generalization on Digits-five

Metric: Accuracy (higher is better)

LeaderboardDataset

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#	Model↕	Accuracy▼	Extra Data	Paper	Date↕	Code
1	Crafting-Shifts(LeNet)	82.61	No	Crafting Distribution Shifts for Validation and ...	2024-09-29	Code
2	ProRandConv (LeNet)	81.35	No	Progressive Random Convolutions for Single Domai...	2023-04-02	-
3	CADA (LeNet)	80.56	No	-	-	-
4	MCL (LeNet)	78.82	No	Meta-causal Learning for Single Domain Generaliz...	2023-04-07	-
5	MetaCNN (LeNet)	78.76	No	-	-	-
6	ABA (LeNet)	76.72	No	Adversarial Bayesian Augmentation for Single-Sou...	2023-07-18	Code
7	L2D (LeNet)	74.46	No	Learning to Diversify for Single Domain Generali...	2021-08-26	Code

#1Crafting-Shifts(LeNet)SOTA
82.61
Accuracy· 2024-09-29
Crafting Distribution Shifts for Validation and Training in Single Source Domain Generalization Code
#2ProRandConv (LeNet)SOTA
81.35
Accuracy· 2023-04-02
Progressive Random Convolutions for Single Domain Generalization
#3CADA (LeNet)
80.56
Accuracy
No paper
#4MCL (LeNet)
78.82
Accuracy· 2023-04-07
Meta-causal Learning for Single Domain Generalization
#5MetaCNN (LeNet)
78.76
Accuracy
No paper
#6ABA (LeNet)
76.72
Accuracy· 2023-07-18
Adversarial Bayesian Augmentation for Single-Source Domain Generalization Code
#7L2D (LeNet)SOTA
74.46
Accuracy· 2021-08-26
Learning to Diversify for Single Domain Generalization Code