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ResearchFormerly WizWand

Image Classification on ImageNet (50:950 split)

0.6536AccuracyMQ-Net
0.4422720.4971360.5520.606864Oct 13, 2022
Updated 8mo ago

Evaluation results

MethodLinks
MQ-NetNoise Ratio=10%2022.100.6536
BADGENoise Ratio=10%2022.100.6484
SIMILARNoise Ratio=10%2022.100.6392
CORESETNoise Ratio=10%2022.100.6364
CONFNoise Ratio=10%2022.100.6356
LLNoise Ratio=10%2022.100.6328
MQ-NetNoise Ratio=20%2022.100.6308
RANDOMNoise Ratio=10%2022.100.6272
CONFNoise Ratio=20%2022.100.6256
CORESETNoise Ratio=20%2022.100.6224
CCALNoise Ratio=10%2022.100.6168
LLNoise Ratio=20%2022.100.6156
BADGENoise Ratio=20%2022.100.6148
SIMILARNoise Ratio=20%2022.100.614
CCALNoise Ratio=20%2022.100.607
RANDOMNoise Ratio=20%2022.100.6012
MQ-NetNoise Ratio=40%2022.100.5695
CCALNoise Ratio=40%2022.100.566
SIMILARNoise Ratio=40%2022.100.5648
LLNoise Ratio=40%2022.100.5568
CORESETNoise Ratio=40%2022.100.5532
MQ-NetNoise Ratio=60%2022.100.5411
RANDOMNoise Ratio=40%2022.100.5404
BADGENoise Ratio=40%2022.100.5404
SIMILARNoise Ratio=60%2022.100.5284
CCALNoise Ratio=60%2022.100.5116
CONFNoise Ratio=40%2022.100.5108
CORESETNoise Ratio=60%2022.100.4904
RANDOMNoise Ratio=60%2022.100.4824
BADGENoise Ratio=60%2022.100.478
LLNoise Ratio=60%2022.100.473
CONFNoise Ratio=60%2022.100.4504