Final Project for CSCI 1470 Deep Learning - Face-Aging with Identity Preserved Conditional GANs

We are implementing the Face Aging With Identity-Preserved Conditional Generative Adversarial Networks paper by Xu Tang, Zongwei Wang, Weixin Luo and Shenghua Gao. We will use GANs to extract low-level features from faces of different ages and encode the features to our images. This is a generative model with a face-aging objective.

Face-aging is a new and exciting field in Deep Learning. With the rising popularity of FaceApp and people’s concerns about privacy, we were drawn to implement our version of an aging-app and think over issues of data privacy, deep fakes and identity theft.

1. Run python download_data.py to download the the Cross-Age Celebrity Dataset metadata(817K) and Face images(3.5G). These will be downloaded and unzipped in the data\ directory.
2. To train the model from scratch, run python3 main.py
3. To test the model and get results from a saved model, run python3 main.py mode=='test'

We have used Google's Deep Learning VM with Pytorch framework to train with GPU. To run locally, follow the directions in setup conda section.

python
from zipfile import ZipFile
zip_file = ZipFile('path_to_file/t.zip', 'r')
zip_file.extractall('path_to_extract_folder')

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh

bash Miniconda3-latest-Linux-x86_64.sh

rm Miniconda3-latest-Linux-x86_64.sh

source .bashrc

conda env create -f environment_gpu.yml

conda env update -f environment_gpu.yml

conda activate clsgan

conda deactivate