Tangent-Normal Adversarial Regularization for Semi-Supervised Learning
A TensorFlow implementation for the paper:
Tangent-Normal Adversarial Regularization for Semi-Supervised Learning
Bing Yu*, Jingfeng Wu*, Jinwen Ma, Zhanxing Zhu
Requirements
- python 3.6
- tensorflow 1.9.0
- numpy
Usage
Generate data
python cifar10_to_numpy.py
Obtain pre-trained VAE
The VAE checkpoint can be obtained via two ways:
- train by yourself:
python train_vae.py - download a pre-trained one: without augmentation, with augmentation
Play with TNAR
All of the parameters should be easy to understand by their naming conventions.
- Training:
python train_tnar.py --resume vae-checkpoint - Test:
python test_tnar.py --resume tnar-checkpoint
Hyperparameters and Performance
See the paper.