Pixel-level 1-D image classification on Sequential MNIST (test) benchmark leaderboard · SOTA2 Research

Pixel-level 1-D image classification on Sequential MNIST (test)

99.72AccuracyCCNN

Evaluation Results

Method	Links
CCNN2022.08		99.72
S52022.08		99.65
S42021.10		99.63
S42022.08		99.63
FlexTCN2022.08		99.62
LSSL2021.10		99.53
LSSL2022.08		99.53
LipschitzRNN2021.10		99.4
LipschitzRNN2022.08		99.4
CKConv2021.10		99.32
CKConv2022.08		99.32
UR-LSTM2021.10		99.28
UR-LSTM2022.08		99.28
UR-GRU2021.10		99.27
UR-GRU2022.08		99.27
TrellisNet2021.10		99.2
TrellisNet2022.08		99.2
TCN2021.10		99
TCN2022.08		99
Dilated GRU2022.08		99
IndRNN2022.08		99
Transformer2021.10		98.9
LSTM2021.10		98.9
HiPPO-RNN2021.10		98.9
Transformer2022.08		98.9
LSTM2022.08		98.9
HIPPO-RNN2022.08		98.9
CCNN2022.08		98.84
LSSL2022.08		98.76
expRNN2022.08		98.7
S42022.08		98.7
S52022.08		98.67
FlexTCN2022.08		98.63
CKConv2022.08		98.54
LMU-FFT2022.08		98.49
r-LSTM2021.10		98.4
r-LSTM2022.08		98.4
UNICORNN2022.08		98.4
HIPPO-RNN2022.08		98.3
TrellisNet2022.08		98.13
Dilated RNN2022.08		98
Transformer2022.08		97.9
TCN2022.08		97.2
LMU2022.08		97.15
UR-LSTM2022.08		96.96
expRNN2022.08		96.6
UR-GRU2022.08		96.51
LipschitzRNN2022.08		96.3
Dilated RNN2022.08		96.1
IndRNN2022.08		96
r-LSTM2022.08		95.2
LSTM2022.08		95.11
Dilated GRU2022.08		94.6