Image Classification on pixel-by-pixel MNIST (test)
99.4AccuracyLSTM w/f STAR
Evaluation Results
| Method | Links | |
|---|---|---|
| LSTM w/f STARNumber of layers=8, units=1282019.11 | 99.4 | |
| STARNumber of layers=8, units=1282019.11 | 99.2 | |
| STARNumber of layers=12, units=1282019.11 | 99.2 | |
| IndRNNunits=1282019.11 | 99 | |
| BN-LSTMunits=1002019.11 | 99 | |
| AntisymmetricRNN w/ gating# units=128, # params=10k2019.02 | 98.8 | |
| GRUNumber of layers=2, units=1282019.11 | 98.8 | |
| AntisymRNNunits=1282019.11 | 98.8 | |
| LSTMNumber of layers=2, units=1282019.11 | 98.4 | |
| RHNNumber of layers=2, units=1282019.11 | 98.4 | |
| STANH-RNNunits=1282019.11 | 98.1 | |
| AntisymmetricRNN# units=128, # params=10k2019.02 | 98 | |
| LSTM# units=128, # params=68k2019.02 | 97.3 | |
| iRNNunits=1002019.11 | 97 | |
| FC uRNN# units=512, # params=270k2019.02 | 96.9 | |
| FC URNNunits=5122019.11 | 96.9 | |
| KRU# units=512, # params=11k2019.02 | 96.4 | |
| uRNNunits=5122019.11 | 95.1 | |
| Soft orthogonal# units=128, # params=18k2019.02 | 94.1 | |
| Soft orthounits=1282019.11 | 94.1 | |
| FC uRNN# units=116, # params=16k2019.02 | 92.8 | |
| Ours+LTraining Dataset=TextureMNIST2023.11 | 61.29 | |
| Ours+LTraining Dataset=ColorMNIST2023.11 | 38.88 | |
| CDEPTraining Dataset=ColorMNIST2023.11 | 33.11 | |
| vRNNNumber of layers=1, units=1282019.11 | 24.3 | |
| BaseTraining Dataset=ColorMNIST2023.11 | 15.87 | |
| BaseTraining Dataset=TextureMNIST2023.11 | 11.35 | |
| CDEPTraining Dataset=TextureMNIST2023.11 | 10.18 |