LVAT

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Latent Space Virtual Adversarial Training (ECCV 2020)

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# LVAT
The code used in the evaluation of Latent Space Virtual Adversarial Training (ECCV 2020 Oral).

## Reference
This code is written based on the original VAT implementation (see [here](https://github.com/takerum/vat_tf)),  
and the implementation for Glow is heavily based on [this code](https://github.com/kmkolasinski/deep-learning-notes/tree/3c7779ea0063896bb3a759efa3e52d173aaae94b/seminars/2018-10-Normalizing-Flows-NICE-RealNVP-GLOW).

## Requirements
tensorflow-gpu 1.14

## Preparation 1. Create symbolic links

```
cd lvat/
# for LVAT-VAE
ln -s ../vae/VAE.py .
ln -s ../vae/config.py .
ln -s ../vae/util .
ln -s ../vae/out_VAE_SVHN/ out_VAE_SVHN
ln -s ../vae/out_VAE_SVHN_aug/ out_VAE_SVHN_aug
ln -s ../vae/out_VAE_CIFAR10/ out_VAE_CIFAR10
ln -s ../vae/out_VAE_CIFAR10_aug/ out_VAE_CIFAR10_aug

# for LVAT-Glow
ln -s ../glow/out/SVHN/w_128__step_22__scale_3__b_128/ out_Glow_SVHN
ln -s ../glow/out/SVHN_aug/w_128__step_22__scale_3__b_128/ out_Glow_SVHN_aug
ln -s ../glow/out/CIFAR10/w_128__step_22__scale_3__b_128/ out_Glow_CIFAR10
ln -s ../glow/out/CIFAR10_aug/w_128__step_22__scale_3__b_128/ out_Glow_CIFAR10_aug
```

## Preparation 2. Create tfrecords

```
cd vae/util/
```
and
``` 
python svhn.py --data_dir=<YOUR_PATH>
```
or
```
python cifar10.py --data_dir=<YOUR_PATH>
```

## Preparation 3. Building transfomer(VAE/Glow)

For VAE,
```
cd vae
python build_AE.py
```
and for Glow,
```
cd glow
python main.py
```
For both, target datasets are identified in config.py (for VAE) or config_glow.py (for Glow).
For VAE, you have to change the directory name where the trained model will be saved so as to accord to the one referred to by the symbolic links created in Preparation 1.
For Glow, the trained model can be referred as it is.

## Training Classifier with LVAT
```
cd lvat
```
and for example
```
python train_semisup.py --data_set=SVHN --num_epochs=200
--epoch_decay_start=80 --epsilon=1.5 --top_bn --method=lvat --log__dir=./out
--data__dir=<YOUR_PATH> --num_iter_per_epoch=400
--batch_size=32 --ul_batch_size=128 --num_labeled_examples=1000
--is_aug=True --ae_type=Glow
```
# Description
- `--data_set` can choose from either `SVHN` or `CIFAR10`.
- <YOUR_PATH> that will be given as `--data__dir` should be the same as the one you identified in the above Preparation 2.
- `--log__dir` is the directory name where the check-point file will be saved.
- `--epsilon` is the magnitude of perturbation, which is commonly used for both `--method=lvat` and `--method=vat`.
- If you set `--metod=vat`, then it works as the original vat.
- You can choose the transfomer from VAE and Glow by `--ae_type=VAE` and `--ae_type=Glow`, respectively.

# Important
For SVHN, `--top_bn` option is necessary to achieve good results.

LVAT

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