Image Classification on permuted MNIST (pMNIST) (test)
97.87AccuracyHNET+R
Evaluation Results
| Method | Links | |
|---|---|---|
| HNET+RContinual Learning Scenario=CL12019.06 | 97.87 | |
| HNET+RContinual Learning Scenario=CL32019.06 | 97.76 | |
| HNET+RContinual Learning Scenario=CL22019.06 | 97.6 | |
| HNET+TIRContinual Learning Scenario=CL32019.06 | 97.59 | |
| HNET+TIRContinual Learning Scenario=CL22019.06 | 97.58 | |
| HNET+ENTContinual Learning Scenario=CL12019.06 | 97.57 | |
| HNET+TIRContinual Learning Scenario=CL12019.06 | 97.57 | |
| DGRContinual Learning Scenario=CL12019.06 | 97.51 | |
| Fine-TuningTask Identity Protocol=TGiven, Evaluation Timing=During training, Backbone=MLP-100,1002021.03 | 97.44 | |
| DGRContinual Learning Scenario=CL22019.06 | 97.35 | |
| su-SNNArchitecture=Spiking Neural Network2026.02 | 97.33 | |
| PosteriorReplay-BbBBackbone=MLP-1000,1000, Stochastic regularization (SR)=true, Evaluation Protocol=TGiven-During2021.03 | 96.92 | |
| PosteriorReplay-BbBBackbone=MLP-1000,1000, Stochastic regularization (SR)=true, Evaluation Protocol=TGiven-Final2021.03 | 96.84 | |
| PosteriorReplay-DiracBackbone=MLP-1000,1000, Stochastic regularization (SR)=true, Evaluation Protocol=TGiven-During2021.03 | 96.73 | |
| Rhythm-SNNsArchitecture=Spiking Neural Network2026.02 | 96.73 | |
| ASRC-SNNArchitecture=Spiking Neural Network2026.02 | 96.62 | |
| DGRContinual Learning Scenario=CL32019.06 | 96.38 | |
| DNC+CUWMemory slots=15, Cache size=10, Controller=Single layer GRU, Hidden vector dimension=1002019.01 | 96.3 | |
| Dilated-RNNLayers=92019.01 | 96.1 | |
| EWCContinual Learning Scenario=CL12019.06 | 95.96 | |
| PosteriorReplay-DiracBackbone=MLP-1000,1000, Stochastic regularization (SR)=true, Evaluation Protocol=TGiven-Final2021.03 | 95.9 | |
| AntisymmetricRNN# units=128, # params=10k2019.02 | 95.8 | |
| PR-DiracTask Identity Protocol=TGiven, Evaluation Timing=During training, Backbone=MLP-100,1002021.03 | 95.7 | |
| DNC+UWMemory slots=15, Controller=Single layer GRU, Hidden vector dimension=1002019.01 | 95.6 | |
| PR-BbBTask Identity Protocol=TGiven, Evaluation Timing=During training, Backbone=MLP-100,1002021.03 | 95.44 | |
| SIContinual Learning Scenario=CL22019.06 | 95.33 | |
| r-LSTM Full BP2019.01 | 95.2 | |
| brf-SNNArchitecture=Spiking Neural Network2026.02 | 95.2 | |
| PR-DiracTask Identity Protocol=TGiven, Evaluation Timing=Final, Backbone=MLP-100,1002021.03 | 95.05 | |
| SIContinual Learning Scenario=CL12019.06 | 94.75 | |
| Dilated-GRULayers=92019.01 | 94.6 | |
| KRU# units=512, # params=11k2019.02 | 94.5 | |
| EWCContinual Learning Scenario=CL22019.06 | 94.42 | |
| PR-BbBTask Identity Protocol=TGiven, Evaluation Timing=Final, Backbone=MLP-100,1002021.03 | 94.35 | |
| PR-RadialTask Identity Protocol=TGiven, Evaluation Timing=During training, Backbone=MLP-100,1002021.03 | 94.31 | |
| PR-RadialTask Identity Protocol=TGiven, Evaluation Timing=Final, Backbone=MLP-100,1002021.03 | 94.3 | |
| SRNNArchitecture=Spiking Neural Network2026.02 | 94.3 | |
| FC uRNN# units=512, # params=270k2019.02 | 94.1 | |
| DNCMemory slots=Best of {15, 30, 60}, Controller=Single layer GRU, Hidden vector dimension=1002019.01 | 94 | |
| PR-SSGETask Identity Protocol=TGiven, Evaluation Timing=During training, Backbone=MLP-100,1002021.03 | 93.58 | |
| AntisymmetricRNN w/ gating# units=128, # params=10k2019.02 | 93.1 | |
| PR-SSGETask Identity Protocol=TGiven, Evaluation Timing=Final, Backbone=MLP-100,1002021.03 | 92.88 | |
| HNET+ENTContinual Learning Scenario=CL22019.06 | 92.8 | |
| LSTM# units=128, # params=68k2019.02 | 92.6 | |
| FC uRNN# units=116, # params=16k2019.02 | 92.1 | |
| HNET+ENTContinual Learning Scenario=CL32019.06 | 91.75 | |
| Soft orthogonal# units=128, # params=18k2019.02 | 91.4 | |
| uRNN2019.01 | 91.4 | |
| GLIFArchitecture=Spiking Neural Network2026.02 | 90.47 | |
| PR-BbBTask Identity Protocol=TInfer, Evaluation Timing=Final, Inference Criterion=Ent, Backbone=MLP-100,1002021.03 | 89.9 | |
| PR-BbBTask Identity Protocol=TInfer, Evaluation Timing=Final, Inference Criterion=Conf, Backbone=MLP-100,1002021.03 | 88.38 | |
| PosteriorReplay-BbBBackbone=MLP-1000,1000, Stochastic regularization (SR)=true, Evaluation Protocol=TInfer-Final (Ent)2021.03 | 85.84 | |
| PosteriorReplay-BbBBackbone=MLP-1000,1000, Stochastic regularization (SR)=true, Evaluation Protocol=TInfer-Final (Conf)2021.03 | 84.35 | |
| IRNN2019.01 | 82 | |
| PR-RadialTask Identity Protocol=TInfer, Evaluation Timing=Final, Inference Criterion=Ent, Backbone=MLP-100,1002021.03 | 81.78 | |
| PosteriorReplay-BbBBackbone=MLP-1000,1000, Stochastic regularization (SR)=true, Evaluation Protocol=TInfer-Final (Agree)2021.03 | 81.33 | |
| PR-RadialTask Identity Protocol=TInfer, Evaluation Timing=Final, Inference Criterion=Conf, Backbone=MLP-100,1002021.03 | 79.87 | |
| PR-SSGETask Identity Protocol=TInfer, Evaluation Timing=Final, Inference Criterion=Ent, Backbone=MLP-100,1002021.03 | 78.94 | |
| PR-SSGETask Identity Protocol=TInfer, Evaluation Timing=Final, Inference Criterion=Conf, Backbone=MLP-100,1002021.03 | 77.43 | |
| PR-RadialTask Identity Protocol=TInfer, Evaluation Timing=Final, Inference Criterion=Agree, Backbone=MLP-100,1002021.03 | 76.73 | |
| PR-DiracTask Identity Protocol=TInfer, Evaluation Timing=Final, Inference Criterion=Ent, Backbone=MLP-100,1002021.03 | 75.84 | |
| PR-DiracTask Identity Protocol=TInfer, Evaluation Timing=Final, Inference Criterion=Conf, Backbone=MLP-100,1002021.03 | 75.14 | |
| PR-BbBTask Identity Protocol=TInfer, Evaluation Timing=Final, Inference Criterion=Agree, Backbone=MLP-100,1002021.03 | 70.52 | |
| PosteriorReplay-DiracBackbone=MLP-1000,1000, Stochastic regularization (SR)=true, Evaluation Protocol=TInfer-Final (Ent)2021.03 | 70.08 | |
| PosteriorReplay-DiracBackbone=MLP-1000,1000, Stochastic regularization (SR)=true, Evaluation Protocol=TInfer-Final (Conf)2021.03 | 69.82 | |
| PR-SSGETask Identity Protocol=TInfer, Evaluation Timing=Final, Inference Criterion=Agree, Backbone=MLP-100,1002021.03 | 68.93 | |
| Fine-TuningTask Identity Protocol=TGiven, Evaluation Timing=Final, Backbone=MLP-100,1002021.03 | 47.89 | |
| EWCContinual Learning Scenario=CL32019.06 | 33.88 | |
| SIContinual Learning Scenario=CL32019.06 | 29.31 |