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Few-Shot Class-Incremental Learning research benchmarks – Page 2 · SOTA2 Research

Few-Shot Class-Incremental Learning

Benchmarks

Dataset NameSOTA MethodSort

Dataset NameSOTA methodMetricTrendResultsLast Updated

CIFAR-100 60 base classes, 5-way 5-shot, Session 0

90.88Accuracy

20

Feb 26, 2026

miniImageNet 60 base classes 5-way 5-shot (incremental)

97.63Session 0 Accuracy

20

Feb 26, 2026

miniImageNet, CUB-200, and CIFAR-100 Average (test)

83.92Base Accuracy

18

Feb 26, 2026

80.9Base Accuracy

17

Mar 4, 2026

3.91Performance Degradation (PD)

16

Mar 5, 2026

9.39PD

16

Mar 5, 2026

NEURON EXPANSION

30.71PD

16

Mar 5, 2026

43.96PD

16

Mar 5, 2026

26.13PD

16

Mar 5, 2026

98.99Accuracy (Session 0)

16

Mar 5, 2026

83.65Base Accuracy

15

Mar 4, 2026

84.6BCR

13

Feb 26, 2026

CUB200 multi-class 5-shot

73.9BCR

13

Feb 26, 2026

iCubWorld multi-class 5-shot

100BCR

13

Feb 26, 2026

CORe50 multi-class 5-shot

99.3BCR

13

Feb 26, 2026

CIFAR100 Single-class five-shot, 1 novel class

84.7BCR

13

Feb 26, 2026

CUB200 Single-class five-shot 1 novel class

74.2BCR

13

Feb 26, 2026

iCubWorld Single-class five-shot, 1 novel class

1BCR

13

Feb 26, 2026

CORe50 Single-class five-shot, 1 novel class

99.8BCR

13

Feb 26, 2026

MiniImageNet (incremental sessions 0-8)

74.66Accuracy (Session 0)

10

May 12, 2026

CUB200 2011 (test)

77.26Accuracy (Session 0)

10

Feb 26, 2026

96.24Accuracy

9

Feb 26, 2026

81.19Accuracy

9

Feb 26, 2026

86.06Accuracy

9

Feb 26, 2026

33.04Avg PD

8

Mar 5, 2026

Page 2 of 4

Popular datasets

ImageNet CIFAR CUB XD-VSCIL iCubWorld

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