Generative Modeling on CIFAR10 (test)
9.62FIDDG flow
Evaluation Results
| Method | Links | |
|---|---|---|
| DG flowRefinement=KL, Backbone=SN-ResNet-GAN2020.12 | 9.62 | |
| DG flowRefinement=log D, Backbone=SN-ResNet-GAN2020.12 | 9.73 | |
| DG flowRefinement=JS, Backbone=SN-ResNet-GAN2020.12 | 9.79 | |
| SN-ResNet-GANRefinement=None (Base Model)2020.12 | 14.1 | |
| DG flowRefinement=KL, Backbone=SN-DCGAN, Loss=non-saturating (ns)2020.12 | 15.3 | |
| DG flowRefinement=KL, Backbone=SN-DCGAN, Loss=hinge (hi)2020.12 | 15.68 | |
| DOTBackbone=SN-DCGAN, Loss=non-saturating (ns)2020.12 | 15.78 | |
| DG flowRefinement=JS, Backbone=SN-DCGAN, Loss=non-saturating (ns)2020.12 | 15.9 | |
| DG flowRefinement=log D, Backbone=SN-DCGAN, Loss=non-saturating (ns)2020.12 | 16.42 | |
| DG flowRefinement=JS, Backbone=SN-DCGAN, Loss=hinge (hi)2020.12 | 16.45 | |
| DOTBackbone=SN-DCGAN, Loss=hinge (hi)2020.12 | 17.12 | |
| DG flowRefinement=log D, Backbone=SN-DCGAN, Loss=hinge (hi)2020.12 | 17.36 | |
| SN-DCGANRefinement=None (Base Model), Loss=hinge (hi)2020.12 | 20.7 | |
| SN-DCGANRefinement=None (Base Model), Loss=non-saturating (ns)2020.12 | 20.9 | |
| DG flowRefinement=JS, Backbone=WGAN-GP2020.12 | 23.15 | |
| DOTBackbone=WGAN-GP2020.12 | 24.14 | |
| DG flowRefinement=log D, Backbone=WGAN-GP2020.12 | 24.53 | |
| DG flowRefinement=KL, Backbone=WGAN-GP2020.12 | 24.68 | |
| WGAN-GPRefinement=None (Base Model)2020.12 | 28.37 |