The implementation of our Additive Adversarial Learning for Unbiased Authentication method by Keras
The code runs with Python and requires Tensorflow of version 1.2.1 or higher and Keras of version 2.0 or higher. Please pip install
the following packages:
numpy
tensorflow
keras
pandas
Download colored_mnist.h5
in https://drive.google.com/drive/folders/1EH9GM9TTsfcWxYV5QtwCtyTtJWrcP3h5
And put colored_mnist.h5
in the data/
folder
For Stage 1, run the following commands in shell:
python train_cmnist_phase1.py
For Stage 2, copy the name of any resulted h5 file, e.g., _colored_mnist_data_phase1_AUC_0.9573.h5
, to set value for read_path
in line 269 of train_phase2.py
, then run the following commands in shell:
python train_phase2.py
See train_cmnist_phase1.py
and train_phase2.py
for details.
Download celeba_img_align_5p_size64.h5
in https://drive.google.com/drive/folders/1EH9GM9TTsfcWxYV5QtwCtyTtJWrcP3h5
And put celeba_img_align_5p_size64.h5
in the data/
folder
For Stage 1, run the following commands in shell:
python train_celeba_phase1.py
For Stage 2, similarly as for CMNIST, copy the name of any resulted h5 file to set value for read_path
in line 269 of train_phase2.py
, then run the following commands in shell:
python train_phase2.py
See train_celeba_phase1.py
and train_phase2.py
for details.
Download device_transfer.h5
in https://drive.google.com/drive/folders/1EH9GM9TTsfcWxYV5QtwCtyTtJWrcP3h5
And put device_transfer.h5
in the data/
folder
For Stage 1, run the following commands in shell:
python train_mobile_phase1.py
For Stage 2, similarly as for CMNIST, copy the name of any resulted h5 file to set value for read_path
in line 269 of train_phase2.py
, then run the following commands in shell:
python train_phase2.py
See train_mobile_phase1.py
and train_phase2.py
for details.