CADA-GAN: Context-Aware GAN with Data Augmentation

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 $\mathbf{\text{C}}$ontext-$\mathbf{\text{A}}$ware GAN that allows optimal feature extraction, with added robustness from additional $\mathbf{\text{D}}$ata $\mathbf{\text{A}}$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 $\mathit{\text{Mean}}\mathit{\text{Squared}}\mathit{\text{Error}}\mathit{\text{Loss}}$ (MSEloss) on latent feature representations and the generated child image is robust compared with the one that generated from baseline models.