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ResearchFormerly WizWand

Image Classification on CIFAR10 4:6 ID-to-OOD (test)

93.1AccuracyMQ-Net
85.123287.194189.26591.3359Oct 13, 2022
Updated 8mo ago

Evaluation results

MethodLinks
MQ-NetNoise Ratio=10%, AL Round=final2022.1093.1
CONFNoise Ratio=10%, AL Round=final2022.1092.83
BADGENoise Ratio=10%, AL Round=final2022.1092.8
MQ-NetNoise Ratio=20%, AL Round=final2022.1092.1
LLNoise Ratio=10%, AL Round=final2022.1092.09
CORESETNoise Ratio=10%, AL Round=final2022.1091.76
BADGENoise Ratio=20%, AL Round=final2022.1091.73
CONFNoise Ratio=20%, AL Round=final2022.1091.72
MQ-NetNoise Ratio=40%, AL Round=final2022.1091.48
LLNoise Ratio=20%, AL Round=final2022.1091.21
CORESETNoise Ratio=20%, AL Round=final2022.1091.06
CCALNoise Ratio=10%, AL Round=final2022.1090.55
CCALNoise Ratio=20%, AL Round=final2022.1089.99
SIMILARNoise Ratio=10%, AL Round=final2022.1089.92
RANDOMNoise Ratio=10%, AL Round=final2022.1089.83
MQ-NetNoise Ratio=60%, AL Round=final2022.1089.51
LLNoise Ratio=40%, AL Round=final2022.1089.41
BADGENoise Ratio=40%, AL Round=final2022.1089.27
SIMILARNoise Ratio=20%, AL Round=final2022.1089.19
CORESETNoise Ratio=40%, AL Round=final2022.1089.12
RANDOMNoise Ratio=20%, AL Round=final2022.1089.06
CCALNoise Ratio=40%, AL Round=final2022.1088.87
CONFNoise Ratio=40%, AL Round=final2022.1088.69
SIMILARNoise Ratio=40%, AL Round=final2022.1088.53
RANDOMNoise Ratio=40%, AL Round=final2022.1087.73
CCALNoise Ratio=60%, AL Round=final2022.1087.49
SIMILARNoise Ratio=60%, AL Round=final2022.1087.38
LLNoise Ratio=60%, AL Round=final2022.1086.95
BADGENoise Ratio=60%, AL Round=final2022.1086.83
CORESETNoise Ratio=60%, AL Round=final2022.1086.5
RANDOMNoise Ratio=60%, AL Round=final2022.1085.64
CONFNoise Ratio=60%, AL Round=final2022.1085.43