  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.
