Semi-supervised classification on MNIST 100 labels
0.0096Error RateADGM
Evaluation Results
| Method | Links | |
|---|---|---|
| ADGM2015.11 | 0.0096 | |
| Ladder Networks2015.11 | 0.0106 | |
| Adversarial Autoencoderstraining=end-to-end2015.11 | 0.019 | |
| CatGAN2015.11 | 0.0191 | |
| VAT2015.11 | 0.0233 | |
| Domain-VAEBackbone=4-layer MLP, Latent dimension (z)=10, Number of labels=100, Number of parameters=~1M2020.11 | 0.027 | |
| SHOT-VAEBackbone=4-layer MLP, Latent dimension (z)=10, Number of labels=100, Number of parameters=~1M2020.11 | 0.0312 | |
| smooth-ELBOBackbone=4-layer MLP, Latent dimension (z)=10, Number of labels=100, Number of parameters=~1M2020.11 | 0.0314 | |
| VAE (M1 + M2)2015.11 | 0.0333 | |
| M1+M2Backbone=4-layer MLP, Latent dimension (z)=10, Number of labels=100, Number of parameters=~1M2020.11 | 0.0333 | |
| Hyperspherical-VAEBackbone=4-layer MLP, Latent dimension (z)=10, Number of labels=100, Number of parameters=~1M2020.11 | 0.052 | |
| Disentangled-VAEBackbone=4-layer MLP, Latent dimension (z)=10, Number of labels=100, Number of parameters=~1M2020.11 | 0.0971 | |
| VAE (M1) + TSVM2015.11 | 0.1182 | |
| VAE (M2)2015.11 | 0.1197 | |
| M2Backbone=4-layer MLP, Latent dimension (z)=10, Number of labels=100, Number of parameters=~1M2020.11 | 0.1197 | |
| NN Baseline2015.11 | 0.258 |