Current child face generators are restricted by the limited size of the available datasets. In addition, feature selection can prove to be a significant challenge, especially due to the large amount of features that need to be trained for. To manage these problems, we proposed CADA-GAN, a \textbf{C}ontext-\textbf{A}ware GAN that allows optimal feature extraction, with added robustness from additional \textbf{D}ata \textbf{A}ugmentation. CADA-GAN is adapted from the popular StyleGAN2-Ada model, with attention on augmentation and segmentation of the parent images. The model has the lowest \textit{Mean Squared Error Loss} (MSEloss) on latent feature representations and the generated child image is robust compared with the one that generated from baseline models.
@article{arxiv.2301.08849,
title = {CADA-GAN: Context-Aware GAN with Data Augmentation},
author = {Sofie Daniels and Jiugeng Sun and Jiaqing Xie},
journal= {arXiv preprint arXiv:2301.08849},
year = {2023}
}