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ResearchFormerly WizWand

Image Classification on CIFAR100 40:60 ID-to-OOD (test)

66.44AccuracyMQ-Net
44.537650.223855.9161.5962Oct 13, 2022
Updated 8mo ago

Evaluation results

MethodLinks
MQ-NetNoise Ratio=10%, AL Round=final2022.1066.44
LLNoise Ratio=10%, AL Round=final2022.1065.08
MQ-NetNoise Ratio=20%, AL Round=final2022.1064.79
LLNoise Ratio=20%, AL Round=final2022.1064.04
CORESETNoise Ratio=10%, AL Round=final2022.1063.79
CONFNoise Ratio=10%, AL Round=final2022.1062.84
BADGENoise Ratio=10%, AL Round=final2022.1062.54
CORESETNoise Ratio=20%, AL Round=final2022.1062.02
BADGENoise Ratio=20%, AL Round=final2022.1061.28
CCALNoise Ratio=10%, AL Round=final2022.1061.2
CCALNoise Ratio=20%, AL Round=final2022.1061.16
RANDOMNoise Ratio=10%, AL Round=final2022.1060.88
CONFNoise Ratio=20%, AL Round=final2022.1060.2
SIMILARNoise Ratio=10%, AL Round=final2022.1060.07
SIMILARNoise Ratio=20%, AL Round=final2022.1059.89
RANDOMNoise Ratio=20%, AL Round=final2022.1059.69
MQ-NetNoise Ratio=40%, AL Round=final2022.1058.96
CCALNoise Ratio=40%, AL Round=final2022.1056.7
LLNoise Ratio=40%, AL Round=final2022.1056.27
CORESETNoise Ratio=40%, AL Round=final2022.1056.21
SIMILARNoise Ratio=40%, AL Round=final2022.1056.13
RANDOMNoise Ratio=40%, AL Round=final2022.1055.52
BADGENoise Ratio=40%, AL Round=final2022.1055.07
CONFNoise Ratio=40%, AL Round=final2022.1053.74
MQ-NetNoise Ratio=60%, AL Round=final2022.1052.82
SIMILARNoise Ratio=60%, AL Round=final2022.1050.61
CCALNoise Ratio=60%, AL Round=final2022.1050.2
LLNoise Ratio=60%, AL Round=final2022.1048.49
CORESETNoise Ratio=60%, AL Round=final2022.1048.33
BADGENoise Ratio=60%, AL Round=final2022.1047.6
RANDOMNoise Ratio=60%, AL Round=final2022.1047.37
CONFNoise Ratio=60%, AL Round=final2022.1045.38