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ResearchFormerly WizWand

1-D Pixel-level Image Classification on sCIFAR (test)

93.15AccuracyMULTIRESNET
59.81868.471577.12585.7785Aug 9, 2022Sep 22, 2022Nov 5, 2022Dec 20, 2022Feb 2, 2023Mar 18, 2023May 2, 2023
Updated 8mo ago

Evaluation results

MethodLinks
MULTIRESNETModel Category=Convolution2023.0593.15
CCNNInput length=10242022.0893.08
Liquid-S4p=32022.0992.02
Liquid-S4Input length=10242022.0892.02
Liquid-S4Model Category=State Space Models2023.0592.02
S4-LegS2022.0991.8
S4Input length=10242022.0891.8
S4Model Category=State Space Models2023.0591.8
S4-(LegS/FouT)2022.0991.58
S4-FouT2022.0991.22
S4D-Inv2022.0990.69
S4DModel Category=State Space Models2023.0590.69
S4D-Lin2022.0990.42
S5Input length=10242022.0890.1
S5Model Category=State Space Models2023.0590.1
S4D-LegS2022.0989.92
S4DInput length=10242022.0889.92
S52022.0989.66
LSSLInput length=10242022.0884.65
FlexConv2022.0980.82
FlexTCNInput length=10242022.0880.82
FlexConvModel Category=Convolution2023.0580.82
UR-GRU2022.0974.4
UR-GRUInput length=10242022.0874.4
UR-GRUModel Category=RNN2023.0574.4
TrellisNet2022.0973.42
TrellisNetInput length=10242022.0873.42
TrellisNetModel Category=Convolution2023.0573.42
r-LSTM2022.0972.2
r-LSTMInput length=10242022.0872.2
r-LSTMModel Category=RNN2023.0572.2
UR-LSTMInput length=10242022.0871
LipschitzRNN2022.0964.2
LipschitzRNNInput length=10242022.0864.2
LipschitzRNNModel Category=RNN2023.0564.2
CKConvInput length=10242022.0863.74
CKConvModel Category=Convolution2023.0563.74
LSTM2022.0963.01
LSTMInput length=10242022.0863.01
LSTMModel Category=RNN2023.0563.01
Transformer2022.0962.2
TransformerInput length=10242022.0862.2
TransformerModel Category=Attention2023.0562.2
HiPPO-RNN2022.0961.1
HIPPO-RNNInput length=10242022.0861.1
HiPPO-RNNModel Category=RNN2023.0561.1