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

93.15Accuracy

MULTIRESNET

Updated 4d ago

Evaluation Results

Method	Links
MULTIRESNET 2023.05		93.15
CCNN 2022.08		93.08
Liquid-S4 2022.09		92.02
Liquid-S4 2022.08		92.02
Liquid-S4 2023.05		92.02
S4-LegS 2022.09		91.8
S4 2022.08		91.8
S4 2023.05		91.8
S4-(LegS/FouT) 2022.09		91.58
S4-FouT 2022.09		91.22
S4D-Inv 2022.09		90.69
S4D 2023.05		90.69
S4D-Lin 2022.09		90.42
S5 2022.08		90.1
S5 2023.05		90.1
S4D-LegS 2022.09		89.92
S4D 2022.08		89.92
S5 2022.09		89.66
LSSL 2022.08		84.65
FlexConv 2022.09		80.82
FlexTCN 2022.08		80.82
FlexConv 2023.05		80.82
UR-GRU 2022.09		74.4
UR-GRU 2022.08		74.4
UR-GRU 2023.05		74.4
TrellisNet 2022.09		73.42
TrellisNet 2022.08		73.42
TrellisNet 2023.05		73.42
r-LSTM 2022.09		72.2
r-LSTM 2022.08		72.2
r-LSTM 2023.05		72.2
UR-LSTM 2022.08		71
LipschitzRNN 2022.09		64.2
LipschitzRNN 2022.08		64.2
LipschitzRNN 2023.05		64.2
CKConv 2022.08		63.74
CKConv 2023.05		63.74
LSTM 2022.09		63.01
LSTM 2022.08		63.01
LSTM 2023.05		63.01
Transformer 2022.09		62.2
Transformer 2022.08		62.2
Transformer 2023.05		62.2
HiPPO-RNN 2022.09		61.1
HIPPO-RNN 2022.08		61.1
HiPPO-RNN 2023.05		61.1