- Algorithms
- Machine learning: LR, SVM, XGBoost, MLP
- Deep learning: CNN, ResNet, VAE, Distilling Knowledge, Data-Free Learning
- Framework
- Sklearn
- Tensorflow
- Pytorch
| Model | Framework | Main Params | Test Accuracy | Time Cost /s | Comments |
|---|---|---|---|---|---|
| LR | sklearn | solver='liblinear', multi_class='ovr' | 0.9202 | 57.87 | |
| SVM | sklearn | kernel='rbf', decision_function_shape='ovr' | 0.9446 | 556.91 | |
| XGBoost | sklearn | max_depth=5, n_jobs=10 | 0.9651 | 141.38 | |
| MLP | sklearn | hidden_layer_sizes=(128, 32) | 0.9768 | 44.80 | |
| MLP | tensorflow | batch_size=512, learning_rate=1e-3, hidden_layers=[128,32] | 0.9795 | 39.05 | |
| CNN | tensorflow | batch_size=256, learning_rate=1e-5, num_epoch=100 | 0.9785 | 1062.03 | |
| ResNet | |||||
| VAE | |||||
| Distilling Knowledge | |||||
| Data-Free Learning |