Classification on SVHN (test)
1.12Error RateColornet-40-48
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| Colornet-40-48No of Parameters=19.0M2019.02 | 1.12 | — | — | |
| DeVries & TaylorNormalization=Yes2019.01 | 1.3 | — | — | |
| BatchNorm + Mixup + CutoutNormalization=Yes2019.01 | 1.4 | — | — | |
| Fixup-init + Mixup + CutoutNormalization=No2019.01 | 1.4 | — | — | |
| Zagoruyko & KomodakisNormalization=Yes2019.01 | 1.5 | — | — | |
| WRN-16-8dropout=true2016.05 | 1.54 | — | — | |
| CoPaNet-RDropout=true, Depth=164, Params=15.7 M, Pathways (k)=2, Width (m)=32017.09 | 1.58 | — | — | |
| RoR-3-WRN58-4+SDParameters=13.3M2016.08 | 1.59 | — | — | |
| DenseNetDropout=false, Depth=100, Params=27.2 M, Growth Rate=242017.09 | 1.59 | — | — | |
| DenseNetGrowth Rate (k)=24, Depth=100, Params=27.2M2016.08 | 1.59 | — | — | |
| Colornet-40-12No of Parameters=1.75M2019.02 | 1.59 | — | — | |
| Mixed max-avg poolingPooling Parameters=1 per layer/channel/region2015.09 | 1.64 | — | — | |
| WRN28-10Parameters=36.5M2016.08 | 1.64 | — | — | |
| Wide ResNet with DropoutDepth=16, Params=2.7M2016.08 | 1.64 | — | — | |
| SimpNet2018.02 | 1.648 | — | — | |
| DenseNetGrowth Rate (k)=12, Depth=100, Params=7.0M2016.08 | 1.67 | — | — | |
| CRMN-28Parameters=>40M2016.08 | 1.68 | — | — | |
| Tree+Max-Avg poolingPooling Parameters=1 per pool layer2015.09 | 1.69 | — | — | |
| Gen. Pool2016.03 | 1.69 | — | — | |
| Lee et al.Normalization=No2019.01 | 1.7 | — | — | |
| CoPaNet-RDropout=true, Depth=164, Params=7.00 M, Pathways (k)=2, Width (m)=22017.09 | 1.72 | — | — | |
| CoPaNetDropout=true, Depth=164, Params=27.9 M, Pathways (k)=2, Width (m)=42017.09 | 1.73 | — | — | |
| DenseNet-BCGrowth Rate (k)=24, Depth=250, Params=15.3M2016.08 | 1.74 | — | — | |
| Densenet-BC-250-24No of Parameters=15.3M2019.02 | 1.74 | — | — | |
| ResNet with stochastic depth2016.03 | 1.75 | — | — | |
| ResNets-110+SDParameters=1.7M, Note=152-layer2016.08 | 1.75 | — | — | |
| Stochastic DepthDropout=false, Depth=110, Params=1.7 M2017.09 | 1.75 | — | — | |
| ResNet with Stochastic DepthDepth=110, Params=1.7M2016.08 | 1.75 | — | — | |
| ResNet with Stochastic Depth2018.02 | 1.75 | — | — | |
| Resnet-101No of Parameters=1.7M2019.02 | 1.75 | — | — | |
| DenseNet-BCGrowth Rate (k)=12, Depth=100, Params=0.8M2016.08 | 1.76 | — | — | |
| Densenet-BC-100-12No of Parameters=0.8M2019.02 | 1.76 | — | — | |
| Recurrent CNN (R-CNN)2015.09 | 1.77 | — | — | |
| RCNN-1922015.11 | 1.77 | — | — | |
| R-CNN2016.03 | 1.77 | — | — | |
| Scalable BO2016.03 | 1.77 | — | — | |
| RCNN-192channels=1922015.11 | 1.77 | — | — | |
| DenseNetGrowth Rate (k)=12, Depth=40, Params=1.0M2016.08 | 1.79 | — | — | |
| HybridNetNb. labeled images=1000, Backbone=ResNet2018.07 | 1.8 | — | — | |
| ResNet with constant depth2016.03 | 1.8 | — | — | |
| MINk (number of maxout pieces)=52015.11 | 1.81 | — | — | |
| Maxout Network In NetworkDropout=true, Params=1.6 M, Pathways (k)=52017.09 | 1.81 | — | — | |
| CoPaNetDropout=true, Depth=164, Params=6.98 M, Pathways (k)=2, Width (m)=22017.09 | 1.83 | — | — | |
| HybridNetNb. labeled images=500, Backbone=ResNet2018.07 | 1.85 | — | — | |
| CoPaNetDropout=true, Depth=164, Params=1.75 M, Pathways (k)=2, Width (m)=12017.09 | 1.86 | — | — | |
| FractalNetParameters=30M2016.08 | 1.87 | — | — | |
| FractalNet with Dropout/Drop-pathDepth=21, Params=38.6M2016.08 | 1.87 | — | — | |
| Fractalnet with dropoutNo of Parameters=38.6M, dropout=true2019.02 | 1.87 | — | — | |
| Our baseline2015.09 | 1.91 | — | — | |
| State-of-the-art2015.03 | 1.92 | — | — | |
| Deeply-Supervised Nets (DSN)2015.09 | 1.92 | — | — | |
| DSN2015.11 | 1.92 | — | — | |
| Deeply Supervised2016.03 | 1.92 | — | — | |
| DSN2016.08 | 1.92 | — | — | |
| Deeply Supervised NetDepth=-, Params=-2016.08 | 1.92 | — | — | |
| Deeply Supervised Net2018.02 | 1.92 | — | — | |
| Deeply-supervised nets2015.11 | 1.92 | — | — | |
| 5 × CONV. NET + DROPCONNECTData Augmentation=true2013.12 | 1.93 | — | — | |
| DropConnect2013.12 | 1.94 | — | — | |
| DropConnect2015.09 | 1.94 | — | — | |
| Dropconnectdata augmentation=true, multiple model voting=true2015.11 | 1.94 | — | — | |
| DropConnect2016.03 | 1.94 | — | — | |
| Drop-connect2015.11 | 1.94 | — | — | |
| Human performance2015.11 | 2 | — | — | |
| FractalNetDepth=21, Params=38.6M2016.08 | 2.01 | — | — | |
| ResNet (reported by [13])Depth=110, Params=1.7M2016.08 | 2.01 | — | — | |
| ResNetreported by=[66] (2016)2018.02 | 2.01 | — | — | |
| FractalnetNo of Parameters=38.6M2019.02 | 2.01 | — | — | |
| Mean Teacher ResNetNb. labeled images=1000, Backbone=ResNet, reproduced_by_authors=true2018.07 | 2.05 | — | — | |
| Multi-digit Number Recognition2013.12 | 2.16 | — | — | |
| Mean Teacher ResNetNb. labeled images=500, Backbone=ResNet, reproduced_by_authors=true2018.07 | 2.33 | — | — | |
| NIN + Dropout2013.12 | 2.35 | — | — | |
| Network in Network (NiN)2015.09 | 2.35 | — | — | |
| NIN2015.11 | 2.35 | — | — | |
| Net in Net2016.03 | 2.35 | — | — | |
| NIN2016.08 | 2.35 | — | — | |
| Network In NetworkDropout=true, Params=0.98 M2017.09 | 2.35 | — | — | |
| Network in NetworkDepth=-, Params=-2016.08 | 2.35 | — | — | |
| Network in Network2018.02 | 2.35 | — | — | |
| Network in Network2015.11 | 2.35 | — | — | |
| N-in-N2019.02 | 2.35 | — | — | |
| CONV. NET + PROBOUTData Augmentation=false2013.12 | 2.39 | — | — | |
| Probabilistic Maxout2015.09 | 2.39 | — | — | |
| FitNet2015.09 | 2.42 | — | — | |
| FitNet2016.08 | 2.42 | — | — | |
| Conv. maxoutdropout=true2013.02 | 2.47 | — | — | |
| Conv. maxout + Dropout2013.12 | 2.47 | — | — | |
| CONV. NET + MAXOUTData Augmentation=false2013.12 | 2.47 | — | — | |
| Maxout Networks2015.09 | 2.47 | — | — | |
| Maxout networkk (number of maxout pieces)=22015.11 | 2.47 | — | — | |
| Maxout2016.03 | 2.47 | — | — | |
| Maxout NetworkDropout=true, Pathways (k)=22017.09 | 2.47 | — | — | |
| Conv. Maxout+DropoutMaxout=true, Dropout=true2015.11 | 2.47 | — | — | |
| Pi modelNumber of labels=73257, Implementation=Replicated/This paper baseline, Architecture=13-layer ConvNet2017.03 | 2.5 | — | — | |
| Mean TeacherNumber of labels=73257, Architecture=13-layer ConvNet2017.03 | 2.5 | — | — | |
| Pi modelNumber of labels=73257, Implementation=Reported in [13], Architecture=13-layer ConvNet2017.03 | 2.54 | — | — | |
| Rectifiersdropout=true, synthetic translation=true2013.02 | 2.68 | — | — | |
| Rectifier + Dropout + Synthetic Translationdata augmentation=synthetic translation2013.12 | 2.68 | — | — | |
| CONV. NET + DROPOUTData Augmentation=true2013.12 | 2.68 | — | — | |
| Temporal EnsemblingNumber of labels=73257, Architecture=13-layer ConvNet2017.03 | 2.74 | — | — |