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

99.72Accuracy

CCNN

Updated 4d ago

Evaluation Results

Method	Links
CCNN 2022.08		99.72
S5 2022.08		99.65
S4 2021.10		99.63
S4 2022.08		99.63
FlexTCN 2022.08		99.62
LSSL 2021.10		99.53
LSSL 2022.08		99.53
LipschitzRNN 2021.10		99.4
LipschitzRNN 2022.08		99.4
CKConv 2021.10		99.32
CKConv 2022.08		99.32
UR-LSTM 2021.10		99.28
UR-LSTM 2022.08		99.28
UR-GRU 2021.10		99.27
UR-GRU 2022.08		99.27
TrellisNet 2021.10		99.2
TrellisNet 2022.08		99.2
TCN 2021.10		99
TCN 2022.08		99
Dilated GRU 2022.08		99
IndRNN 2022.08		99
Transformer 2021.10		98.9
LSTM 2021.10		98.9
HiPPO-RNN 2021.10		98.9
Transformer 2022.08		98.9
LSTM 2022.08		98.9
HIPPO-RNN 2022.08		98.9
CCNN 2022.08		98.84
LSSL 2022.08		98.76
expRNN 2022.08		98.7
S4 2022.08		98.7
S5 2022.08		98.67
FlexTCN 2022.08		98.63
CKConv 2022.08		98.54
LMU-FFT 2022.08		98.49
r-LSTM 2021.10		98.4
r-LSTM 2022.08		98.4
UNICORNN 2022.08		98.4
HIPPO-RNN 2022.08		98.3
TrellisNet 2022.08		98.13
Dilated RNN 2022.08		98
Transformer 2022.08		97.9
TCN 2022.08		97.2
LMU 2022.08		97.15
UR-LSTM 2022.08		96.96
expRNN 2022.08		96.6
UR-GRU 2022.08		96.51
LipschitzRNN 2022.08		96.3
Dilated RNN 2022.08		96.1
IndRNN 2022.08		96
r-LSTM 2022.08		95.2
LSTM 2022.08		95.11
Dilated GRU 2022.08		94.6