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ResearchFormerly WizWand

Image Classification on ImageNet ID-to-OOD 50:950 (test)

65.36AccuracyMQ-Net
44.227249.713655.260.6864Oct 13, 2022
Updated 8mo ago

Evaluation results

MethodLinks
MQ-NetNoise Ratio=10%, AL Round=final2022.1065.36
BADGENoise Ratio=10%, AL Round=final2022.1064.84
SIMILARNoise Ratio=10%, AL Round=final2022.1063.92
CORESETNoise Ratio=10%, AL Round=final2022.1063.64
CONFNoise Ratio=10%, AL Round=final2022.1063.56
LLNoise Ratio=10%, AL Round=final2022.1063.28
MQ-NetNoise Ratio=20%, AL Round=final2022.1063.08
RANDOMNoise Ratio=10%, AL Round=final2022.1062.72
CONFNoise Ratio=20%, AL Round=final2022.1062.56
CORESETNoise Ratio=20%, AL Round=final2022.1062.24
CCALNoise Ratio=10%, AL Round=final2022.1061.68
LLNoise Ratio=20%, AL Round=final2022.1061.56
BADGENoise Ratio=20%, AL Round=final2022.1061.48
SIMILARNoise Ratio=20%, AL Round=final2022.1061.4
CCALNoise Ratio=20%, AL Round=final2022.1060.7
RANDOMNoise Ratio=20%, AL Round=final2022.1060.12
MQ-NetNoise Ratio=40%, AL Round=final2022.1056.95
CCALNoise Ratio=40%, AL Round=final2022.1056.6
SIMILARNoise Ratio=40%, AL Round=final2022.1056.48
LLNoise Ratio=40%, AL Round=final2022.1055.68
CORESETNoise Ratio=40%, AL Round=final2022.1055.32
MQ-NetNoise Ratio=60%, AL Round=final2022.1054.11
RANDOMNoise Ratio=40%, AL Round=final2022.1054.04
BADGENoise Ratio=40%, AL Round=final2022.1054.04
SIMILARNoise Ratio=60%, AL Round=final2022.1052.84
CCALNoise Ratio=60%, AL Round=final2022.1051.16
CONFNoise Ratio=40%, AL Round=final2022.1051.08
CORESETNoise Ratio=60%, AL Round=final2022.1049.04
RANDOMNoise Ratio=60%, AL Round=final2022.1048.24
BADGENoise Ratio=60%, AL Round=final2022.1047.8
LLNoise Ratio=60%, AL Round=final2022.1047.3
CONFNoise Ratio=60%, AL Round=final2022.1045.04