Neural Network exam's project. Keras implementation of the work done in:
Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images by V. Mirjalili, S. Raschka, A. M. Namboodiri, and A. Ross. https://arxiv.org/abs/1712.00321
Our work is based on the preprocessed Celeb-A dataset provided by iPRoBe-lab's implementation
For more details about this project read our final report.
First you need to download and preprocess dataset.
Our work is based on the Celeb-A dataset provided by iPRoBe-lab's implementation
Place both images-dpmcrop-train and images-dpmcrop-test folders in repo's root and just run
cd src/pre_processing
python prepare_dataset_celeba.py
At the end of pre-processing the dataset folder will be structured as follows:
dataset
|
+-- celebA
| |
| +-- test
| | +-- female
| | +-- male
| +-- train
| | +-- female
| | +-- male
| +-- validation
| | +-- female
| | +-- male
+-- prototype
In this phase models of the Semi-Adversarial-Network are trained independently. AutoEncoder and Gender-Predictor have been pre-trained by us.
cd src/
For pretraining the Autoencoder
python pretrain_autencoder.py
For pretraining the Gender Classifier
python pretrain_genderclassifier.py
Face-Matcher uses downloaded weights (in the same way of the reference paper) during Further-Training so we didn't need to pre-train it.
Obtained weights can be found here: https://drive.google.com/file/d/1OgnvG74Qru9tbeXImUldcr60HuJoLrov/view?usp=sharing
Train the autoencoder using other modules' feedbacks.
The complete Semi-Adversarial model (modules/san.py) is formed by the following NNs:
- AutoEncoder
- Gender Classifier
- Face Matcher
Face Matcher and Gender Classifier are not trainable components during this phase.
Be sure to have placed pretrained weights in /weights and named them as follows:
- autoencoder_pretrain_weights.h5
- genderclassifier_pretrain_weights.h5
- facematcher_pretrain_weights.h5
These are the only weights considered by further_train.py, so if you retrain one of the modules, please rename obtained
weights accordingly or manage weights loading as you prefer in each modules in /src/modules.
Then you can run further train with
cd src/
python further_train.py
Obtained weights will be saved in /weights with a timestamp attached to the file name.
Obtained weights can be found here:
https://drive.google.com/file/d/1O4hjK8BBsD922SVnFEkPDitE4MEZ5ss8/view?usp=sharing
This project was developed with passion by: Bruno Marino & Gianluca Pepe.