Image Classification on SVHN (test) (Top-1 Error Rate)
2.03Top-1 Error RateDash (RA)
Evaluation Results
| Method | Links | |
|---|---|---|
| Dash (RA)Number of labels=1000, Backbone=Wide ResNet-28-2, Augmentation strategy=RandAugment2021.09 | 2.03 | |
| Dash (CTA)Number of labels=1000, Backbone=Wide ResNet-28-2, Augmentation strategy=CTAugment2021.09 | 2.14 | |
| Dash (RA)Number of labels=250, Backbone=Wide ResNet-28-2, Augmentation strategy=RandAugment2021.09 | 2.17 | |
| FixMatch (RA)Number of labels=1000, Backbone=Wide ResNet-28-2, Augmentation strategy=RandAugment2021.09 | 2.28 | |
| RYS (UDA)Number of labels=1000, Backbone=Wide ResNet-28-22021.09 | 2.32 | |
| RYS (FixMatch)Number of labels=1000, Backbone=Wide ResNet-28-22021.09 | 2.34 | |
| FixMatch (CTA)Number of labels=1000, Backbone=Wide ResNet-28-2, Augmentation strategy=CTAugment2021.09 | 2.36 | |
| Dash (CTA)Number of labels=250, Backbone=Wide ResNet-28-2, Augmentation strategy=CTAugment2021.09 | 2.38 | |
| RYS (UDA)Number of labels=250, Backbone=Wide ResNet-28-22021.09 | 2.45 | |
| UDANumber of labels=1000, Backbone=Wide ResNet-28-22021.09 | 2.46 | |
| FixMatch (RA)Number of labels=250, Backbone=Wide ResNet-28-2, Augmentation strategy=RandAugment2021.09 | 2.48 | |
| RYS (FixMatch)Number of labels=250, Backbone=Wide ResNet-28-22021.09 | 2.63 | |
| FixMatch (CTA)Number of labels=250, Backbone=Wide ResNet-28-2, Augmentation strategy=CTAugment2021.09 | 2.64 | |
| ReMixMatchNumber of labels=1000, Backbone=Wide ResNet-28-22021.09 | 2.65 | |
| ReMixMatchNumber of labels=250, Backbone=Wide ResNet-28-22021.09 | 2.92 | |
| Dash (RA)Number of labels=40, Backbone=Wide ResNet-28-2, Augmentation strategy=RandAugment2021.09 | 3.03 | |
| Dash (CTA)Number of labels=40, Backbone=Wide ResNet-28-2, Augmentation strategy=CTAugment2021.09 | 3.14 | |
| ReMixMatchNumber of labels=40, Backbone=Wide ResNet-28-22021.09 | 3.34 | |
| Mean TeacherNumber of labels=1000, Backbone=Wide ResNet-28-22021.09 | 3.42 | |
| MixMatchNumber of labels=1000, Backbone=Wide ResNet-28-22021.09 | 3.5 | |
| Mean TeacherNumber of labels=250, Backbone=Wide ResNet-28-22021.09 | 3.57 | |
| FixMatch (RA)Number of labels=40, Backbone=Wide ResNet-28-2, Augmentation strategy=RandAugment2021.09 | 3.96 | |
| MixMatchNumber of labels=250, Backbone=Wide ResNet-28-22021.09 | 3.98 | |
| UDANumber of labels=250, Backbone=Wide ResNet-28-22021.09 | 5.69 | |
| Pi-ModelNumber of labels=1000, Backbone=Wide ResNet-28-22021.09 | 7.54 | |
| FixMatch (CTA)Number of labels=40, Backbone=Wide ResNet-28-2, Augmentation strategy=CTAugment2021.09 | 7.65 | |
| Pseudo-LabelingNumber of labels=1000, Backbone=Wide ResNet-28-22021.09 | 9.94 | |
| Pi-ModelNumber of labels=250, Backbone=Wide ResNet-28-22021.09 | 18.96 | |
| Pseudo-LabelingNumber of labels=250, Backbone=Wide ResNet-28-22021.09 | 20.21 | |
| MixMatchNumber of labels=40, Backbone=Wide ResNet-28-22021.09 | 42.55 | |
| UDANumber of labels=40, Backbone=Wide ResNet-28-22021.09 | 52.63 |