List your company
ResearchFormerly WizWand

Image Classification on CIFAR-10 (4:6 split)

0.931AccuracyMQ-Net
0.8512320.8719410.892650.913359Oct 13, 2022
Updated 8mo ago

Evaluation results

MethodLinks
MQ-NetNoise Ratio=10%2022.100.931
CONFNoise Ratio=10%2022.100.9283
BADGENoise Ratio=10%2022.100.928
MQ-NetNoise Ratio=20%2022.100.921
LLNoise Ratio=10%2022.100.9209
CORESETNoise Ratio=10%2022.100.9176
BADGENoise Ratio=20%2022.100.9173
CONFNoise Ratio=20%2022.100.9172
MQ-NetNoise Ratio=40%2022.100.9148
LLNoise Ratio=20%2022.100.9121
CORESETNoise Ratio=20%2022.100.9106
CCALNoise Ratio=10%2022.100.9055
CCALNoise Ratio=20%2022.100.8999
SIMILARNoise Ratio=10%2022.100.8992
RANDOMNoise Ratio=10%2022.100.8983
MQ-NetNoise Ratio=60%2022.100.8951
LLNoise Ratio=40%2022.100.8941
BADGENoise Ratio=40%2022.100.8927
SIMILARNoise Ratio=20%2022.100.8919
CORESETNoise Ratio=40%2022.100.8912
RANDOMNoise Ratio=20%2022.100.8906
CCALNoise Ratio=40%2022.100.8887
CONFNoise Ratio=40%2022.100.8869
SIMILARNoise Ratio=40%2022.100.8853
RANDOMNoise Ratio=40%2022.100.8773
CCALNoise Ratio=60%2022.100.8749
SIMILARNoise Ratio=60%2022.100.8738
LLNoise Ratio=60%2022.100.8695
BADGENoise Ratio=60%2022.100.8683
CORESETNoise Ratio=60%2022.100.865
RANDOMNoise Ratio=60%2022.100.8564
CONFNoise Ratio=60%2022.100.8543