This repository was created with the aim of incorporating an Explanatory System in the Conditional Adversarial Autoencoder (CAAE). The main source of inspiration was the paper Age Progression/Regressionby Conditional Adversarial Autoencoder proposed by (Zhang, Song, et al.). Moreover, the paper xAI-GAN: Enhancing Generative Adversarial Networks via Explainable AI Systems proposed by (Nagisetyy, Graves, et al.) formed the base of adding xAI methods in CAAE. Endly, thanks to Mattan Serry and the AgeProgression work, we devoloped our code in PyTorch.
This code was used to implement the experiments of my Master Thesis in Digital Media - Computational Intelligence.
The dataset we used for training was the CACD + UTKFace which consists of 21267 images in 7 age classes. FGNET with 1002 images used for the test purpose. The data folder can be downloaded through Google Drive.
- Python 3.7
- Pytorch 1.2.0
- Captum 0.4.0
- Lime 0.2.0.1
The control and change of the Explainable Artificial Intelligence system is achieved through the net.teachSplit() in mainCAAEsplit_ver2_lime.py script. If it is a need to train the Original CAAE, without the addition of the xAI system, explainable is set to False. The xAI system is activated by setting explainable to True. LIME can be added by setting the explanation_type to 'lime'.
conda create --name <env> --file requirements.txtpython mainCAAEsplit_ver2_lime.py --mode train --epochs 200 --input data/CACD_UTKFace --output checkpointspython mainCAAEsplit_ver2_lime.py --mode test --load checkpoints/epoch200 --input data/FGNET --output results/checkpoints/epoch200 --age <0 or 1> --gender <0 or 1>Christos Korgialas and Constantine Kotropoulos