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Digit Classification research benchmarks · SOTA2 Research

Digit Classification

Benchmarks

Dataset NameSOTA MethodSort

Dataset NameSOTA methodMetricTrendResultsLast Updated

Regularization of neural networks

0.21Error Rate

94

Feb 26, 2026

MNIST -> USPS (test)

98.5Accuracy

65

Feb 27, 2026

Digit-Five (test)

92.97Average Accuracy

60

Feb 26, 2026

USPS → MNIST target (test)

98.7Accuracy

58

Feb 27, 2026

95.4Median Accuracy

47

Jun 29, 2026

75.2Median QDA Accuracy

47

Jun 29, 2026

99.1Accuracy

38

Feb 27, 2026

SVHN → MNIST target (test)

Target Supervised

99.2Accuracy

37

Feb 27, 2026

98.2Accuracy

34

Feb 27, 2026

SVHN to MNIST (test)

99.4Accuracy

33

Feb 26, 2026

100Adversarial Accuracy

30

Feb 26, 2026

distributionally robust learning framework

0.944Accuracy

28

Feb 27, 2026

N-MNIST Binary-grid

99.64Accuracy

27

Feb 26, 2026

Standard Learning

99.7Accuracy

24

Mar 17, 2026

Biased MNIST unbiased (test)

96.02Accuracy

24

Feb 26, 2026

Colored MNIST foreground color (test)

86.37Unbiased Accuracy

24

Feb 26, 2026

MNIST -> SVHN (test)

Fully Supervised

85Accuracy

21

Feb 26, 2026

MNIST 10-way 10-shot

98.51Accuracy

20

Feb 26, 2026

MNIST 10-way 5-shot

98.51Accuracy

20

Feb 26, 2026

MNIST 10-way 1-shot

97.31Accuracy

20

Feb 26, 2026

DomainNet (test)

42.3Accuracy

18

Feb 26, 2026

RotatedMNIST (test)

88.9Accuracy

18

Feb 26, 2026

Proposed Protection

17,770Additional Arithmetic Operations

15

Apr 24, 2026

SYND → MNIST S45 (target)

94.97Target Domain Accuracy

14

Feb 27, 2026

SYND → MNIST S20 (target)

95.88Accuracy (Target Domain)

14

Feb 27, 2026

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