1-D Pixel-level Image Classification on sCIFAR (test) benchmark leaderboard · SOTA2 Research

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

93.15AccuracyMULTIRESNET

Evaluation Results

Method	Links
MULTIRESNET2023.05		93.15
CCNN2022.08		93.08
Liquid-S42022.09		92.02
Liquid-S42022.08		92.02
Liquid-S42023.05		92.02
S4-LegS2022.09		91.8
S42022.08		91.8
S42023.05		91.8
S4-(LegS/FouT)2022.09		91.58
S4-FouT2022.09		91.22
S4D-Inv2022.09		90.69
S4D2023.05		90.69
S4D-Lin2022.09		90.42
S52022.08		90.1
S52023.05		90.1
S4D-LegS2022.09		89.92
S4D2022.08		89.92
S52022.09		89.66
LSSL2022.08		84.65
FlexConv2022.09		80.82
FlexTCN2022.08		80.82
FlexConv2023.05		80.82
UR-GRU2022.09		74.4
UR-GRU2022.08		74.4
UR-GRU2023.05		74.4
TrellisNet2022.09		73.42
TrellisNet2022.08		73.42
TrellisNet2023.05		73.42
r-LSTM2022.09		72.2
r-LSTM2022.08		72.2
r-LSTM2023.05		72.2
UR-LSTM2022.08		71
LipschitzRNN2022.09		64.2
LipschitzRNN2022.08		64.2
LipschitzRNN2023.05		64.2
CKConv2022.08		63.74
CKConv2023.05		63.74
LSTM2022.09		63.01
LSTM2022.08		63.01
LSTM2023.05		63.01
Transformer2022.09		62.2
Transformer2022.08		62.2
Transformer2023.05		62.2
HiPPO-RNN2022.09		61.1
HIPPO-RNN2022.08		61.1
HiPPO-RNN2023.05		61.1